Hiroyuki SATO
| Department of Informatics | Professor |
| Cluster I (Informatics and Computer Engineering) | Professor |
| Artificial Intelligence eXploration Research Center | Professor |
Researcher Information
Degree
Research Keyword
Career
- Apr. 2024 - Present
The University of Electro-Communications, Graduate School of Informatics and Engineering, School of Informatics and Engineering Department of Informatics, Cluster I(Informatics and Computer Engineering), Professor - Apr. 2016 - Mar. 2024
The University of Electro-Communications, Department of Informatics, Graduate School of Informatics and Engineering, Associate Professor, Japan - Apr. 2010 - Mar. 2016
The University of Electro-Communications, Department of Informatics, Faculty of Informatics and Engineering, Graduate School of Informatics and Engineering, Assistant Professor, Japan - Apr. 2011 - Jun. 2011
CINVESTAV-IPN (Centro de Investigación y de Estudios Avanzados del IPN), Departamento de Computación, Visiting Researcher, Mexico - Apr. 2009 - Mar. 2010
The University of Electro-Communications, Department of Human Communication, Faculty of Electro-Communications, Assistant Professor, Japan
Educational Background
- Apr. 2006 - Mar. 2009
Shinshu University, The Interdisciplinary Graduate School of Science and Technology, Department of Mathematics and System Development, Doctor of Engineering, Japan - Apr. 2003 - Mar. 2005
Shinshu University, The Division of Science and Technology, Department of Electrical and Electronic Engineering, Master of Engineering, Japan - Apr. 1999 - Mar. 2003
Shinshu University, Faculty of Engineering, Department of Electrical and Electronic Engineering, Bachelor of Engineering, Japan
Member History
- 2022 - Present
Chair, IEEE CIS Task Force on Many-Objective Optimisation - Apr. 2021 - Present
運営委員会 運営委員, 情報処理学会 数理モデル化と問題解決研究会 - Jul. 2015 - Present
編集委員, Swarm and Evolutionary Computation (ELSEVIER) - Mar. 2012 - Present
論文誌編集員, 進化計算学会 - Mar. 2012 - Present
専門委員, 進化計算学会 - 2011 - Present
Program Committee, Genetic and Evolutionary Computation Conference - 2010 - Present
Technical Program Committee, IEEE Congress on Evolutionary Computation - Apr. 2023 - Mar. 2025
Editorial Advisory Board, Transactions on Electrical and Electronic Engineering, The Institute of Electrical Engineers of Japan (IEEJ) - Jun. 2024 - Jul. 2024
On-Line Conference Co-Chairs, IEEE WCCI2024 - Jul. 2022 - Jul. 2023
EMO Track chair, Genetic and Evolutionary Computation Conference - Apr. 2019 - Mar. 2023
編集委員, 情報処理学会 論文誌 数理モデル化と応用編集委員会 - Dec. 2021 - Nov. 2022
論文誌, 進化計算シンポジウム2021特集号編集委員, 進化計算学会 - Oct. 2017 - Sep. 2021
理事(研究会担当), 進化計算学会 - Jun. 2020 - Mar. 2021
論文誌ジャーナル/JIP 査読委員, 情報処理学会 - Apr. 2017 - Mar. 2021
幹事, 情報処理学会 数理モデル化と問題解決研究会 - Jun. 2016 - May 2020
編集委員（基盤グループ）, 情報処理学会 論文誌 ジャーナル編集委員会（WG） - Jun. 2016 - May 2020
論文誌ジャーナル/JIP編集委員（Computing Group）, 情報処理学会 - Dec. 2018
Program Committee Vice Chair, SCIS&ISIS2018 - Sep. 2018
2018 JPNSEC International Workshop on Evolutionary Computation, Program Chair, JPNSEC - Jul. 2018
Local Arrangement, Local Financial Chair, Student Affair Chair, GECCO2018(Genetic and Evolutionary Computation Conference) - Apr. 2013 - Mar. 2017
運営委員会 運営委員, 情報処理学会 数理モデル化と問題解決研究会 - Dec. 2016
進化計算シンポジウム2016 プログラム委員長, 進化計算学会 - Dec. 2015
進化計算シンポジウム2015 実行委員, 進化計算学会 - Mar. 2014
第6回進化計算学会研究会 実行委員長, 進化計算学会 - Dec. 2012
進化計算シンポジウム2012 実行委員, 進化計算学会 - Dec. 2012
論文誌 進化型多目的最適化特集号 副編集委員長, 進化計算学会 - Apr. 2010 - Mar. 2012
専門委員, 人工知能学会進化計算フロンティア研究会
Research Activity Information
Award
- Dec. 2023
進化計算学会, 当該学術雑誌において，審査によって毎年原則1件に与えられる賞．
Niching Migratory Multi-Swarm Optimization for Multiple Local Minimum Search: Towards Constant population and Concurrent Search by Physical Robots
論文誌論文賞, Yusuke Maekawa;Kodai Kawano;Sho Kajihara;Yukiko Fukumoto;Hiroyuki Sato;Keiki Takadama
Official journal - Dec. 2023
進化計算学会, 当該学術雑誌において，審査によって毎年原則1件に与えられる賞．
Misclassification Detection and Correction based on Conditional VAE for Rule Evolution in Learning Classifier System
論文誌論文賞, Hiroki Shiraishi;Masakazu Tadokoro;Yohei Hayamizu;Yukiko Fukumoto;Hiroyuki Sato;Keiki Takadama
Official journal - Dec. 2022
進化計算学会, 当該学術雑誌において，審査によって毎年原則1件に与えられる賞．
A Multi-objective Evolutionary Algorithm Using Weight Vector Arrangement Based on Pareto Front Estimation
論文誌論文賞, Tomoaki Takagi;Keiki Takadama;Hiroyuki Sato
Official journal - Jul. 2022
2022 Genetic and Evolutionary Computation Conference (GECCO2022), The best paper in the respective track. Several papers were initially nominated throughout the paper review, and a vote among conference attendees determined the final selection.
Absumption based on Overgenerality and Condition-Clustering based Specialization for XCS with Continuous-Valued Inputs
Best Paper Award, Hiroki Shiraishi;Yohei Hayamizu;Hiroyuki Sato;Keiki Takadama
International society - Oct. 2021
Journal of Advanced Computational Intelligence and Intelligent Informatics (JACIII), 当該学術雑誌において，審査によって毎年原則1件に与えられる賞．
Self-Structured Cortical Learning Algorithm by Dynamically Adjusting Columns and Cells
Best Paper Award, Sotetsu Suzugamine;Takeru Aoki;Keiki Takadama;Hiroyuki Sato
Official journal - Dec. 2020
進化計算学会論文誌, 当該学術雑誌において，審査によって毎年原則1件に与えられる賞．
Simulation-based Evolutionary Multi-objective Optimization of Air conditioning Schedule in Office Building
論文誌論文賞, Yoshihiro Ohta;Hiroyuki Sato
Official journal - Dec. 2019
進化計算学会
進化計算シンポジウム2019 ベストポスター発表賞 - Aug. 2019
日本知能情報ファジィ学会
貢献賞 - Dec. 2018
2018 Joint 10th International Conference on Soft Computing and Intelligent Systems and 19th International Symposium on Advanced Intelligent Systems (SCIS&ISIS2018)
Best Paper Award - Nov. 2017
計測自動制御学会 システム・情報部門 学術講演会 SSI2017
優秀論文賞 - Nov. 2016
計測自動制御学会 システム・情報部門
論文誌論文賞 - Dec. 2015
進化計算学会論文誌
論文誌論文賞 - Dec. 2015
ISCBI2015 (3rd International Symposium on Computational and Business Intelligence)
Best Oral Presentation Award - Jul. 2014
2014 Genetic and Evolutionary Computation Conference (GECCO 2014)
Best Paper Award - Dec. 2012
進化計算学会論文誌
論文誌論文賞 - Dec. 2012
進化計算シンポジウム2012
最優秀発表賞 - Dec. 2011
IEEE Computational Intelligence Society Japan Chapter
Young Researcher Award - Jul. 2011
2011 Genetic and Evolutionary Computation Conference (GECCO 2011)
Best Paper Award - Jun. 2011
人工知能学会
2010年度研究会優秀賞 - Oct. 2010
IEEE Shinetsu Section
Young Researcher Paper Award - Mar. 2008
(財)船井情報科学振興財団
Digital Courier船井若手奨励賞 - Mar. 2007
情報処理学会 第63回数理モデル化と問題解決(MPS)研究会
プレゼンテーション賞 - Apr. 2004
2004 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP'04)
Students Paper Award
Paper
- Generating High-Dimensional Prototypes with a Classifier System by Evolving in Latent Space
Naoya Yatsu; Hiroki Shiraishi; Hiroyuki Sato; Keiki Takadama
Genetic and Evolutionary Computation Conference (GECCO 2024), to appear, Jul. 2024, Peer-reviwed
International conference proceedings, English - Approximating Pareto Local Optimal Solution Networks
Shoichiro Tanaka; Gabriela Ochoa; Arnaud Liefooghe; Keiki Takadama; Hiroyuki Sato
Last, Genetic and Evolutionary Computation Conference (GECCO 2024), to appear, Jul. 2024, Peer-reviwed, with international co-author(s)
International conference proceedings, English - Prototype Generation with sUpervised Classifier system on kNN matching
Naoya Yatsu; Hiroki Shiraishi; Hiroyuki Sato; Keiki Takadama
2024 IEEE World Congress on Computational Intelligence (IEEE WCCI 2024), (to appear), Jun. 2024, Peer-reviwed
International conference proceedings, English - Design of Generalized and Specialized Helper Objectives for Multi-objective Continuous Optimization Problems
Keigo Mochizuki; Tomoki Ishizuka; Naoya Yatsu; Hiroyuki Sato; Keiki Takadama
2024 IEEE World Congress on Computational Intelligence (IEEE WCCI 2024), (to appear), Jun. 2024, Peer-reviwed
International conference proceedings, English - From multipoint search to multiarea search: Novelty-based multi-objectivization for unbounded search space optimization
Ryuki Ishizawa; Hiroyuki Sato; Keiki Takadama
2024 IEEE World Congress on Computational Intelligence (IEEE WCCI 2024), (to appear), Jun. 2024, Peer-reviwed
International conference proceedings, English - Designing Helper Objectives in Multi-objectivization
Shoichiro Tanaka; Arnaud Liefooghe; Keiki Takadama; Hiroyuki Sato
Last, 2024 IEEE World Congress on Computational Intelligence (IEEE WCCI 2024), (to appear), Jun. 2024, Peer-reviwed, with international co-author(s)
International conference proceedings, English - Pareto Front Estimation Model Optimization for Aggregative Solution Set Representation
Naru Okumura; Keiki Takadama; Hiroyuki Sato
Last, 2024 IEEE World Congress on Computational Intelligence (IEEE WCCI 2024), (to appear), Jun. 2024, Peer-reviwed
International conference proceedings, English - Push and Pull Search with Directed Mating for Constrained Multi-objective Optimization
Ryo Takamiya; Minami Miyakawa; Keiki Takadama; Hiroyuki Sato
Last, 2024 IEEE World Congress on Computational Intelligence (IEEE WCCI 2024), (to appear), Jun. 2024, Peer-reviwed
International conference proceedings, English - Should Multi-objective Evolutionary Algorithms Use Always Best Non-dominated Solutions as Parents?
Kazuma Sato; Naru Okumura; Keiki Takadama; Hiroyuki Sato
Last, 2024 IEEE World Congress on Computational Intelligence (IEEE WCCI 2024), (to appear), Jun. 2024, Peer-reviwed
International conference proceedings, English - Evolutionary Constrained Multi-Factorial Optimization Based on Task Similarity
Shio Kawakami; Keiki Takadama; Hiroyuki Sato
Last, 2024 IEEE World Congress on Computational Intelligence (IEEE WCCI 2024), (to appear), Jun. 2024, Peer-reviwed
International conference proceedings, English - Multi-Layer Cortical Learning Algorithm for Forecasting Time-Series Data with Smoothly Changing Variation Patterns
Kazushi Fujino; Keiki Takadama; Hiroyuki Sato
Last, 2024 IEEE World Congress on Computational Intelligence (IEEE WCCI 2024), (to appear), Jun. 2024, Peer-reviwed
International conference proceedings, English - 目的関数の推定類似度を用いる進化計算による多因子最適化
川上 紫央; 高玉 圭樹; 佐藤 寛之
Last, 進化計算学会論文誌, 14, 1, 40-54, Dec. 2023, Peer-reviwed
Scientific journal - Adaptive Action-prediction Cortical Learning Algorithm Under Uncertain Environments
Kazushi Fujino; Takeru Aoki; Keiki Takadama; Hiroyuki Sato
Last, International Journal of Hybrid Intelligent Systems, IOS Press, 225-245, Nov. 2023, Peer-reviwed, The cortical learning algorithm (CLA) is a time series prediction algorithm. Memory elements called columns and cells discretely represent data with their state combinations, whereas linking elements called synapses change their state combinations. For tasks requiring to take actions, the action-prediction CLA (ACLA) has an advantage to complement missing state values with their predictions. However, an increase in the number of missing state values (i) generates excess synapses negatively affect the action predictions and (ii) decreases the stability of data representation and makes the output of action values difficult. This paper proposes an adaptive ACLA using (i) adaptive synapse adjustment and (ii) adaptive action-separated decoding in an uncertain environment, missing multiple input state values probabilistically. (i) The proposed adaptive synapse adjustment suppresses unnecessary synapses. (ii) The proposed adaptive action-separated decoding adaptively outputs an action prediction separately for each action value. Experimental results using uncertain two- and three-dimensional mountain car tasks show that the proposed adaptive ACLA achieves a more robust action prediction performance than the conventional ACLA, DDPG, and the three LSTM-assisted reinforcement learning algorithms of DDPG, TD3, and SAC, even though the number of missing state values and their frequencies increase. These results implicate that the proposed adaptive ACLA is a way to making decisions for the future, even in cases where information surrounding the situation partially lacked.
Scientific journal, English - A Biased Multi-objective Item Stock Optimization Using Evolutionary Algorithms
Kazuma Sato; Yasuyuki Mitsui; Shio Kawakami; Shoichiro Tanaka; Hiroyuki Sato
Last, The 5th ASEAN-UEC Workshop on Informatics and Engineering, 1-2, Sep. 2023, Peer-reviwed
International conference proceedings, English - A Preliminary Study on Spatially Distributed Data Forecast Using Cortical Learning Algorithm
Kazuma Niwa; Takeru Aoki; Kazushi Fujino; Keiki Takadama; Hiroyuki Sato
Last, The 5th ASEAN-UEC Workshop on Informatics and Engineering, 1-2, Sep. 2023, Peer-reviwed
International conference proceedings, English - A Preliminary Study on Ensemble Pareto Front Estimation
Kosuke Kikawada; Tomoaki Takagi; Naru Okumura; Keiki Takadama; Hiroyuki Sato
Last, The 5th ASEAN-UEC Workshop on Informatics and Engineering, 1-2, Sep. 2023, Peer-reviwed
International conference proceedings, English - A Preliminary Study on Solution Generation for Evolutionary Multi-factorial Optimization
Shio Kawakami; Keiki Takadama; Hiroyuki Sato
Last, The 5th ASEAN-UEC Workshop on Informatics and Engineering, 1-2, Sep. 2023, Peer-reviwed
International conference proceedings, English - Exploring High-dimensional Rules Indirectly via Latent Space Through a Dimensionality Reduction for XCS
Naoya Yatsu; Hiroki Shiraishi; Hiroyuki Sato; Keiki Takadama
Genetic and Evolutionary Computation Conference (GECCO 2023), 606-614, Jul. 2023, Peer-reviwed
International conference proceedings, English - Toward Unbounded Search Space Exploration by Particle Swarm Optimization in Multi-modal Optimization Problem
Ryuki Ishizawa; Tomoya Kuga; Yusuke Maekawa; Hiroyuki Sato; Keiki Takadama
2023 IEEE Congress on Evolutionary Computation (CEC2023), 1-8, Jul. 2023, Peer-reviwed
International conference proceedings - Pareto Front Upconvert on Multi-objective Building Facility Control Optimization
Naru Okumura; Tomoaki Takagi; Yoshihiro Ohta; Hiroyuki Sato
Last, Genetic and Evolutionary Computation Conference (GECCO 2023) Companion, 1963-1971, Jul. 2023, Peer-reviwed
International conference proceedings - Multilayered Cortical Learning Algorithm for Forecasting Time-series Data with Probabilistically Changing Trends
Kazushi Fujino; Takeru Aoki; Keiki Takadama; Hiroyuki Sato
Last, Journal of Signal Processing, Research Institute of Signal Processing, Japan, 27, 4, 69-73, Jul. 2023, Peer-reviwed
Scientific journal - Multi-objectivization Relaxes Multi-funnel Structures in Single-objective NK-landscapes
Shoichiro Tanaka; Keiki Takadama; Hiroyuki Sato
Last, Evolutionary Computation in Combinatorial Optimization, Springer Nature Switzerland, 195-210, Apr. 2023, Peer-reviwed
International conference proceedings - Multi-layer Cortical Learning Algorithm for Trend Changing Time-series Forecast
Kazushi Fujino; Takeru Aoki; Keiki Takadama; Hiroyuki Sato
Last, 2023 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP2023), 281-284, Mar. 2023, Peer-reviwed
International conference proceedings - Similarity-based Multi-factorial Evolutionary Algorithm for Binary Optimization Problems
Shio Kawakami; Keiki Takadama; Hiroyuki Sato
Last, 2023 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP2023), 372-375, Mar. 2023, Peer-reviwed
International conference proceedings - Preliminary Study of Adaptive Synapse Generation in Cortical Learning Algorithm
Takeru Aoki; Keiki Takadama; Hiroyuki Sato
Last, 2023 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP2023), 392-395, Mar. 2023, Peer-reviwed
International conference proceedings - Pareto Front Upconvert by Iterative Estimation Modeling and Solution Sampling
Tomoaki Takagi; Keiki Takadama; Hiroyuki Sato
Evolutionary Multi-Criterion Optimization (EMO 2023), Springer Nature Switzerland, 218-230, Mar. 2023, Peer-reviwed
International conference proceedings - Adaptive Synapse Adjustment and Decoding in Action-prediction Cortical Learning Algorithm
Kazushi Fujino; Takeru Aoki; Keiki Takadama; Hiroyuki Sato
The 14th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2022), Springer Nature Switzerland, 811-821, Mar. 2023, Peer-reviwed
International conference proceedings - Directional Pareto Front and Its Estimation to Encourage Multi-objective Decision-making
Tomoaki Takagi; Keiki Takadama; Hiroyuki Sato
Last, IEEE Access, Institute of Electrical and Electronics Engineers (IEEE), 11, 20619-20634, Feb. 2023, Peer-reviwed
Scientific journal - Niching Migratory Multi-swarm Optimizer by Replacing Generation and Deletion with Movement for Real Robots: Adjusting Swarm Size Based on Convergence
Yusuke Maeakwa; Hiroyuki Sato; Keiki Takadama
The 16th International Symposium on Distributed Autonomous Robotic Systems (DARS 2022), Nov. 2022, Peer-reviwed
International conference proceedings - Synergistic Effect of Adaptive Synapse Arrangement and Column-based Decoder in Cortical Learning Algorithm
Takeru Aoki; Keiki Takadama; Hiroyuki Sato
Last, 2022 Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems (SCIS&ISIS), IEEE, 1-6, Nov. 2022, Peer-reviwed
International conference proceedings - Solution Archive and Its Re-use in Evolutionary Many-objective Facility Control Optimization
Naru Okumura; Yoshihiro Ohta; Hiroyuki Sato
Last, 2022 Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems (SCIS&ISIS), IEEE, 1-6, Nov. 2022, Peer-reviwed
International conference proceedings - Adaptive Synapse Adjustment for Multivariate Cortical Learning Algorithm
Kazushi Fujino; Takeru Aoki; Keiki Takadama; Hiroyuki Sato
Last, 2022 Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems (SCIS&ISIS), IEEE, 1-6, Nov. 2022, Peer-reviwed
International conference proceedings - マルコフ連鎖に基づく局所解ネットワークと(1+1)-EAの性能予測
田中 彰一郎; 古谷 博史; 日和 悟; 廣安 知之; 高玉 圭樹; 佐藤 寛之
進化計算学会誌, 13, 1, 40-52, Oct. 2022, Peer-reviwed
Scientific journal, Japanese - 進化計算による在庫配置最適化
三井 康行; 山越 悠貴; 佐藤 寛之
進化計算学会誌, 13, 1, 66-76, Oct. 2022, Peer-reviwed
Scientific journal, Japanese - Can the Same Rule Representation Change Its Matching Area? Enhancing Representation in XCS for Continuous Space by Probability Distribution in Multiple Dimension
Hiroki Shiraishi; Yohei Hayamizu; Hiroyuki Sato; Keiki Takadama
Genetic and Evolutionary Computation Conference (GECCO 2022),, 431-439, Jul. 2022, Peer-reviwed
International conference proceedings, English - オープンスペースディスカッション2021実施報告
能島 裕介; 高木 英行; 棟朝 雅晴; 濱田 直希; 西原 慧; 高玉 圭樹; 佐藤 寛之; 桐淵 大貴; 宮川 みなみ
進化計算学会誌, 13, 1, 1-9, Jul. 2022, Peer-reviwed
Scientific journal, Japanese - Digital Twin Based Evolutionary Building Facility Control Optimization
Kohei Fukuhara; Ryo Kumagai; Fukawa Yuta; Tanabe Shin-ichi; Hiroki Kawano; Yoshihiro Ohta; Hiroyuki Sato
Last, 2022 IEEE Congress on Evolutionary Computation (CEC), IEEE, 1-8, Jul. 2022, Peer-reviwed
International conference proceedings - XCSR with VAE using Gaussian Distribution Matching: From Point to Area Matching in Latent Space for Less-overlapped Rule Generation in Observation Space
Naoya Yatsu; Hiroki Shiraishi; Hiroyuki Sato; Keiki Takadama
2022 IEEE Congress on Evolutionary Computation (CEC), IEEE, 1-8, Jul. 2022, Peer-reviwed
International conference proceedings - Absumption Based on Overgenerality and Condition-clustering Based Specialization for XCS with Continuous-valued Inputs
Hiroki Shiraishi; Yohei Hayamizu; Hiroyuki Sato; Keiki Takadama
Genetic and Evolutionary Computation Conference (GECCO 2022), ACM, 422-430, Jul. 2022, Peer-reviwed
International conference proceedings - Inheritance vs. Expansion: Generalization Degree of Nearest Neighbor Rule in Continuous Space as Covering Operator of XCS
Hiroki Shiraishi; Yohei Hayamizu; Iko Nakari; Hiroyuki Sato; Keiki Takadama
Applications of Evolutionary Computation, Springer International Publishing, 352-368, Apr. 2022, Peer-reviwed
International conference proceedings - 航空機着陸問題における混雑時に対応するクラスタリングを用いた分割反復最適化手法
村田 暁紀; 佐藤 寛之; 髙玉 圭樹; デライエ・ダニエル
電気学会論文誌Ｃ(電子・情報・システム部門誌), 142, 2, 198-205, Feb. 2022, Peer-reviwed
Scientific journal, Japanese - 推定パレートフロントに基づいて重みベクトル群を配置する多目的進化アルゴリズム
高木 智章; 高玉 圭樹; 佐藤 寛之
Last, 進化計算学会誌, 45-60, Jan. 2022, Peer-reviwed
Scientific journal, Japanese - VBAとGASを用いたシフト表自動化アプリの作成とシフト組合せ最適化用の遺伝的アルゴリズムの検討
富田 一光; 佐藤 寛之; 奥野 剛史
進化計算学会誌, 88-97, Jan. 2022, Peer-reviwed
Scientific journal, Japanese - Multi-factorial Evolutionary Algorithm Using Objective Similarity Based Parent Selection
Shio Kawakami; Keiki Takadama; Hiroyuki Sato
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, Springer International Publishing, 45-60, Jan. 2022, Peer-reviwed
International conference proceedings - Variable Elite Population in Grid-based Multi-objective Evolutionary Optimization
Kensuke Kano; Tomoaki Takagi; Shoichiro Tanaka; Keiki Takadama; Hiroyuki Sato
The 3rd ASEAN-UEC Workshop on Informatics and Engineering for SDGs, 1-2, Dec. 2021, Peer-reviwed
International conference proceedings, English - Preliminary Study of Multi-factorial Evolutionary Algorithm Using Objective Similarities on Different Objective Landscapes
Shio Kawakami; Keiki Takadama; Hiroyuki Sato
The 3rd ASEAN-UEC Workshop on Informatics and Engineering for SDGs, 1-2, Dec. 2021, Peer-reviwed
International conference proceedings, English - Cortical Learning Based Action-decision Under Uncertain Environment
Kazushi Fujino; Takeru Aoki; Keiki Takadama; Hiroyuki Sato
The 3rd ASEAN-UEC Workshop on Informatics and Engineering for SDGs, 1-2, Dec. 2021, Peer-reviwed
International conference proceedings, English - A Forgetting Mechanism in Cortical Learning Algorithm for Time-series Forecast
Yuki Goto; Takeru Aoki; Keiki Takadama; Hiroyuki Sato
The 3rd ASEAN-UEC Workshop on Informatics and Engineering for SDGs, 1-2, Dec. 2021, Peer-reviwed
International conference proceedings, English - 時間的・空間的観点に基づく粒子群最適化と差分進化の個体別選択
河野航大; 梶原奨; 髙野諒; 佐藤寛之; 髙玉圭樹
進化計算学会誌, 1-11, Dec. 2021, Peer-reviwed
Scientific journal, Japanese - Double-layered Cortical Learning Algorithm for Time-series Data Prediction
Takeru Aoki; Keiki Takadama; Hiroyuki Sato
The 13th EAI International Conference on Bio-inspired Information and Communications Technologies (BICT2021), 33-44, Sep. 2021, Peer-reviwed
International conference proceedings, English - Maintaining Soundness of Social Network by Understanding Fake News Dissemination and People’s Belief
Risa Kusano; Kento Yoshikawa; Hiroyuki Sato; Masatsugu Ichino; Hiroshi Yoshiura
The 15th International Workshop on Informatics (IWIN2021), 1-7, Sep. 2021, Peer-reviwed
International conference proceedings, English - Misclassification Detection Based on Conditional VAE for Rule Evolution in Learning Classifier System
Hiroki Shiraishi; Masakazu Tadokoro; Yohei Hayamizu; Yukiko Fukumoto; Hiroyuki Sato; Keiki Takadama
2021 Genetic and Evolutionary Computation Conference (GECCO 2021), 169-170, Jul. 2021, Peer-reviwed
International conference proceedings, English - Adaptive Synapse Arrangement in Cortical Learning Algorithm
Takeru Aoki; Keiki Takadama; Hiroyuki Sato
Journal of Advanced Computational Intelligence and Intelligent Informatics (JACIII), 25, 4, 450-466, Jul. 2021, Peer-reviwed
Scientific journal, English - Weight Vector Arrangement Using Virtual Objective Vectors in Decomposition-based MOEA
Tomoaki Takagi; Keiki Takadama; Hiroyuki Sato
2021 IEEE Congress on Evolutionary Computation (CEC2021), 1462-1469, Jun. 2021, Peer-reviwed
International conference proceedings, English - XCS with Weight-based Matching in VAE Latent Space and Additional Learning of High-dimensional Data
Masakazu Tadokoro; Hiroyuki Sato; Keiki Takadama
2021 IEEE Congress on Evolutionary Computation (CEC2021), 304-310, Jun. 2021, Peer-reviwed
International conference proceedings, English - Increasing Accuracy and Interpretability of High-dimensional Rules for Learning Classifier System
Hiroki Shiraishi; Masakazu Tadokoro; Yohei Hayamizu; Yukiko Fukumoto; Hiroyuki Sato; Keiki Takadama
2021 IEEE Congress on Evolutionary Computation (CEC2021), 311-318, Jun. 2021, Peer-reviwed
International conference proceedings, English - Generating Duplex Routes for Robust Bus Transport Network by Improved Multi-objective Evolutionary Algorithm Based on Decomposition
Sho Kajihara; Hiroyuki Sato; Keiki Takadama
Applications of Evolutionary Computation, LNCS, Vol. 12694, Springer-Verlag,, 65-80, Apr. 2021, Peer-reviwed
International conference proceedings, English - Distance Minimization Problems for Multi-Factorial Evolutionary Optimization Benchmarking
Shio Kawakami; Tomoaki Takagi; Keiki Takadama; Hiroyuki Sato
The 12th World Congress on Nature and Biologically Inspired Computing (NaBIC 2020), Hybrid Intelligent Systems. HIS 2020, Advances in Intelligent Systems and Computing, Vol. 1375, Springer, Cham,, 710-719, Apr. 2021, Peer-reviwed
International conference proceedings, English - Alzheimer Dementia Detection Based on Circadian Rhythm Disorder of Heartrate
Naoya Matsuda; Iko Nakari; Ryotaro Arai; Hiroyuki Sato; Keiki Takadama; Masanori Hirose; Hiroshi Hasegawa; Makoto Shiraishi; Takahide Matsuda
2021 IEEE 3rd Global Conference on Life Sciences and Technologies (LifeTech 2021),, 364-368, Mar. 2021, Peer-reviwed
International conference proceedings, English - 評価値軸・設計変数上の解の継続変化に対する群知能アルゴリズムのためのメカニズムの設計とその追従性の評価
高野諒; 佐藤寛之; 高玉圭樹
進化計算学会誌, 29-44, Mar. 2021, Peer-reviwed
Scientific journal, Japanese - Pareto Front Estimation Using Distance from Unit Hyperplane
Tomoaki Takagi; Keiki Takadama; Hiroyuki Sato
The 11th Edition of International Conference Series on Evolutionary Multi-Criterion Optimization (EMO2021), 126-138, Mar. 2021, Peer-reviwed
International conference proceedings, English - Finding Many Good Solutions by Multi-swarm Optimization for Multiple Robots: The Niching Migratory Multi-swarm Optimizer with Limited Movement
Yusuke Maekawa; Kodai Kawano; Sho Kajihara; Yukiko Fukumoto; Hiroyuki Sato; Keiki Takadama
The 26th International Symposium on Artificial Life and Robotics (AROB 2021),, 486-491, Jan. 2021, Peer-reviwed
International conference proceedings, English - 学習分類子システムのルール進化に対するConditional VAEに基づく誤判定検知・訂正
白石 洋輝; 田所 優和; 速水 陽平; 福本 有季子; 佐藤 寛之; 高玉 圭樹
進化計算学会論文誌, 12, 3, 98-111, Jan. 2021, Peer-reviwed
Scientific journal, Japanese - 実ロボット適用に向けた複数局所解探索のための複数群間移動に基づく群知能最適化
前川 裕介; 河野 航大; 梶原 奨; 福本 有季子; 佐藤 寛之; 高玉 圭樹
進化計算学会論文誌, 12, 3, 125-136, Jan. 2021, Peer-reviwed
Scientific journal, Japanese - Column-based Decoder of Internal Prediction Representation in Cortical Learning Algorithms
Takeru Aoki; Keiki Takadama; Hiroyuki Sato
Joint 11th International Conference on Soft Computing and Intelligent Systems and 21st International Symposium on Advanced Intelligent Systems (SCIS&ISIS2020), 1-7, Dec. 2020, Peer-reviwed
International conference proceedings, English - A Preliminary Study on a Multi-layered Cortical Learning Algorithm
Takeru Aoki; Keiki Takadama; Hiroyuki Sato
The 7th UEC Seminar in ASEAN, 2020 and The 2nd ASEAN-UEC Workshop on Energy and AI, on web, 1-2, Nov. 2020, Peer-reviwed
International conference proceedings, English - A Study on Multi-objective Evolutionary Stage Generation Using MarioGAN
Ryo Kumagai; Tomoaki Takagi; Keiki Takadama; Hiroyuki Sato
The 7th UEC Seminar in ASEAN, 2020 and The 2nd ASEAN-UEC Workshop on Energy and AI, on web, 1-2, Nov. 2020, Peer-reviwed
International conference proceedings, English - A Study on Multivariate CLA Complementing Missing Time-series Data
Akihiko Nagashima; Takeru Aoki; Keiki Takadama; Hiroyuki Sato
The 7th UEC Seminar in ASEAN, 2020 and The 2nd ASEAN-UEC Workshop on Energy and AI, on web, 1-2, Nov. 2020, Peer-reviwed
International conference proceedings, English - Local Covering: Adaptive Rule Generation Method Using Existing Rules for XCS
Masakazu Tadokoro; Satoshi Hasegawa; Takato Tatsumi; Hiroyuki Sato; Keiki Takadama
Proc. 2020 IEEE Congress on Evolutionary Computation (CEC2020), 1-8, Jul. 2020, Peer-reviwed
International conference proceedings, English - Evolutionary Air-Conditioning Optimization Using an LSTM-Based Surrogate Evaluator
Yoshihiro Ohta; Takafumi Sasakawa; Hiroyuki Sato
2020 IEEE Congress on Evolutionary Computation (CEC2020), 1-8, Jul. 2020, Peer-reviwed
International conference proceedings, English - Non-dominated Solution Sampling Using Environmental Selection in EMO algorithms
Tomoaki Takagi; Keiki Takadama; Hiroyuki Sato
2020 IEEE Congress on Evolutionary Computation (CEC2020), 1-9, Jul. 2020, Peer-reviwed
International conference proceedings, English - Incremental Lattice Design of Weight Vector Set
Tomoaki Takagi; Keiki Takadama; Hiroyuki Sato
Workshop on Decomposition Techniques in Evolutionary Optimization, 2020 Genetic and Evolutionary Computation Conference (GECCO 2020), 1486-1494, Jul. 2020, Peer-reviwed
International conference proceedings, English - Visual Mapping of Multi-objective Optimization Problems and Evolutionary Algorithms
Kohei Yamamoto; Tomoaki Takagi; Keiki Takadama; Hiroyuki Sato
Workshop on Visualisation Methods in Genetic and Evolutionary Computation, 2020 Genetic and Evolutionary Computation Conference (GECCO 2020), 1872-1879, Jul. 2020, Peer-reviwed
International conference proceedings, English - Preliminary Study of Adaptive Grid-based Decomposition on Many-objective Evolutionary Optimization
Kensuke Kano; Tomoaki Takagi; Keiki Takadama; Hiroyuki Sato
Workshop on Evolutionary Many-objective Optimization, 2020 Genetic and Evolutionary Computation Conference (GECCO 2020), 1373-1380, Jul. 2020, Peer-reviwed
International conference proceedings, English - Self-Structured Cortical Learning Algorithm by Dynamically Adjusting Columns and Cells
Sotetsu Suzugamine; Takeru Aoki; Keiki Takadama; Hiroyuki Sato
Journal of Advanced Computational Intelligence and Intelligent Informatics (JACIII), 24, 2, 185-198, Mar. 2020, Peer-reviwed
Scientific journal, English - Niche Radius Adaptation in Bat Algorithm for Locating Multiple Optima in Multimodal Functions
Takuya Iwase; Ryo Takano; Fumito Uwano; Hiroyuki Sato; Keiki Takadama
2019 IEEE Congress on Evolutionary Computation (CEC2019),, 770-784, 2019, Peer-reviwed
International conference proceedings, English - Sleep Apnea Syndrome Detection Based on Biological Vibration Data from Mattress Sensor
Iko Nakari; Akinori Murata; Eiki Kitajima; Hiroyuki Sato; Keiki Takadama
The 2019 IEEE Symposium Series on Computational Intelligence (SSCI 2019), 549-555, 2019, Peer-reviwed
International conference proceedings, English - Complex-valued-based Learning Classifier System for POMDP Environments
Keiki Takadama; Daichi Yamazaki; Masaya Nakata; Hiroyuki Sato
2019 IEEE Congress on Evolutionary Computation (CEC2019), 1853-1860, 2019, Peer-reviwed
International conference proceedings, English - Knowledge Extraction from XCSR Based on Dimensionality Reduction and Deep Generative Models
Masakazu Tadokoro; Satoshi Hasegawa; Takato Tatsumi; Hiroyuki Sato; Keiki Takadama
2019 IEEE Congress on Evolutionary Computation (CEC2019), 1884-1891, 2019, Peer-reviwed
International conference proceedings, English - Simultaneous Local Adaptation for Different Local Properties
Ryota Kobayashi; Ryo Takano; Hiroyuki Sato; Keiki Takadama
Adaptation, Learning and Optimization, 12, 216-227, 2019, Peer-reviwed
International conference proceedings, English - Evolving Generalized Solutions for Robust Multi-objective Optimization: Transportation Analysis in Disaster
Keiki Takadama; Keiji Sato; Hiroyuki Sato
Evolutionary Multi-Criterion Optimization, in Deb, K., Goodman, E., Coello Coello, C., Klamroth, K., Miettinen, K., Mostaghim, S., Reed, P. (Eds.), Lecture Notes in Computer Science, 11411, 491-503, 2019, Peer-reviwed
International conference proceedings, English - Niche Radius Adaptation in Bat Algorithm for Locating Multiple Optima in Multimodal Functions.
Takuya Iwase; Ryo Takano; Fumito Uwano; Hiroyuki Sato; Keiki Takadama
IEEE Congress on Evolutionary Computation, CEC 2019, Wellington, New Zealand, June 10-13, 2019, IEEE, 800-807, 2019, Peer-reviwed
International conference proceedings - Bat Algorithm with Dynamic Niche Radius for Multimodal Optimization
Takuya Iwase; Ryo Takano; Fumito Uwano; Hiroyuki Sato; Keiki Takadama
Proceedings of the 3rd International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence (ISMSI 2019), 8-13, 2019, Peer-reviwed
International conference proceedings, English - Artificial Bee Colony Algorithm Based on Adaptive Local Information Sharing Meets Multiple Dynamic Environments
Ryo Takano; Hiroyuki Sato; Keiki Takadama
SICE Journal of Control, Measurement, and System Integration (JCMSI), Informa UK Limited, 1, 1-10, 2019, Peer-reviwed
Scientific journal, English - A Distribution Control of Weight Vector Set for Multi-objective Evolutionary Algorithms
Tomoaki Takagi; Keiki Takadama; Hiroyuki Sato
Proc. of 11th EAI International Conference on Bio-inspired Information and Communications Technologies (BICT2019), 70-80, 2019, Peer-reviwed
International conference proceedings, English - Evolutionary Optimization of Air-conditioning Schedule Robust for Temperature Forecast Errors
Yoshihiro Ohta; Hiroyuki Sato
Proc. 2019 IEEE Congress on Evolutionary Computation (CEC2019), 2483-2490, 2019, Peer-reviwed
International conference proceedings, English - A Study for Parallelization of Multi-objective Evolutionary Algorithm Based on Decomposition and Directed Mating
Minami Miyakawa; Hiroyuki Sato; Yuji Sato
Proc. of 2019 3rd International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence (ISMSI 2019), 1-6, 2019, Peer-reviwed
International conference proceedings, English - オフィスビルにおける空調スケジュールのシミュレーションに基づく進化型多目的最適化
太田恵大; 佐藤寛之
進化計算学会論文誌, 10, 2, 22-32, 2019, Peer-reviwed
Scientific journal, Japanese - Evaluation of Simultaneously Optimizing Multiple Models in Vehicle Design Using Distributed NSGA-II Sharing Extreme Non-dominated Solutions
Mikiko Sato; Minami Miyakawa; Hiroyuki Sato; Yuji Sato
International Journal of Mathematical Models and Methods in Applied Sciences, 227-235, 2018, Peer-reviwed
Scientific journal, English - Novelty Search-based Bat Algorithm: Adjusting Distance among Solutions for Multimodal Optimization
Takuya Iwase; Ryo Takano; Fumito Uwano; Hiroyuki Sato; Keiki Takadama
Proceedings of the 22nd Asia Pacific Symposium on Intelligent and Evolutionary Systems, 29-36, 2018, Peer-reviwed
International conference proceedings, English - Towards Adaptive Aircraft Landing Order with Aircraft Routes Partially Fixed by Air Traffic Controllers as Human Intervention
Akinori Murata; Hiroyuki Sato; Keiki Takadama
SICE Journal of Control, Measurement, and System Integration (JCMSI), 11, 2, 105-112, 2018, Peer-reviwed
Scientific journal, English - Infeasible Solution Repair and MOEA/D Sharing Weight Vectors for Solving Multi-objective Set Packing Problems
Mariko Tanaka; Yuki Yamagishi; Hidetoshi Nagai; Hiroyuki Sato
Proc. of Late-Breaking Abstracts at 2018 Genetic and Evolutionary Computation Conference (GECCO 2018), ACM, 71-72, 2018, Peer-reviwed
International conference proceedings, English - Directed Mating in Decomposition-based MOEA for Constrained Many-objective Optimization
Minami Miyakawa; Hiroyuki Sato; Yuji Sato
Proc. of 2018 Genetic and Evolutionary Computation Conference (GECCO 2018), 721-728, 2018, Peer-reviwed
International conference proceedings, English - Evolutionary Multi-objective Air-Conditioning Schedule Optimization for Office Buildings
Yoshihiro Ohta; Hiroyuki Sato
Proc. of 2018 Genetic and Evolutionary Computation Conference (GECCO 2018), 296-297, 2018, Peer-reviwed
International conference proceedings, English - Effects of Chain-reaction Initial Solution Arrangement in Decomposition-Based MOEAs
Hiroyuki Sato; Minami Miyakawa; Keiki Takadama
Post Proceedings of EUROSIM 2016, Linköping Electronic Conference Proceedings, 1-6, 2018, Peer-reviwed
International conference proceedings, English - Effects of Duplication Operator in Evolutionary Simultaneous Design Optimization of Multiple Cars.
Minami Miyakawa; Hiroyuki Sato; Haruka Matsumoto; Mariko Tanaka; Mikiko Sato; Yuji Sato
Proc. of Joint 10th International Conference on Soft Computing and Intelligent Systems and 19th International Symposium on Advanced Intelligent Systems (SCIS&ISIS2018), IEEE, 267-274, 2018, Peer-reviwed
International conference proceedings, English - A Study on a Cortical Learning Algorithm Dynamically Adjusting Columns and Cells
Sotetsu Suzugamine; Takeru Aoki; Keiki Takadama; Hiroyuki Sato
Proc. of Joint 10th International Conference on Soft Computing and Intelligent Systems and 19th International Symposium on Advanced Intelligent Systems (SCIS&ISIS2018), 478-484, 2018, Peer-reviwed
International conference proceedings, English - A Preliminary Study on Weight Vector Distribution Control Based on Intermediate Objective Value
Tomoaki Takagi; Keiki Takadama; Hiroyuki Sato
Proc. of 2018 JPNSEC International Workshop on Evolutionary Computation, 20-26, 2018
International conference proceedings, English - An Infeasible Solution Repair Switching Addition and Deletion Operations for Evolutionary Multi-Objective Set Packing Optimization
Mariko Tanaka; Hiroyuki Sato
Proc. of 2018 JPNSEC International Workshop on Evolutionary Computation, 109-112, 2018
International conference proceedings, English - A Comparative Study of Evaluation Methods for Infeasible Solutions on Constrained MOEA/D with Directed Mating and Archives
Minami Miyakawa; Hiroyuki Sato; Yuji Sato
Proc. of 2018 JPNSEC International Workshop on Evolutionary Computation, 63-70, 2018
International conference proceedings, English - Artificial Bee Colony Algorithm Based on Adaptive Local Information Sharing: Approach for Several Dynamic Changes
Ryo Takano; Hiroyuki Sato; Keiki Takadama
Proc. of Genetic and Evolutionary Computation Conference (GECCO 2018), 95-96, 2018, Peer-reviwed
International conference proceedings, English - Multiple Swarm Intelligence Methods Based on Multiple Population with Sharing Best Solution for Drastic Environmental Change
Yuta Umenai; Fumito Uwano; Hiroyuki Sato; Keiki Takadama
Proc. of Genetic and Evolutionary Computation Conference (GECCO 2018), 97-98, 2018, Peer-reviwed
International conference proceedings, English - XCSR Based on Compressed Input by Deep Neural Network for High Dimensional Data
Kazuma Matsumoto; Ryo Takano; Takato Tatsumi; Hiroyuki Sato; Tim Kovacs; Keiki Takadama
Proc. of International Workshop on Learning Classifier Systems (IWLCS 2018), in Genetic and Evolutionary Computation Conference (GECCO 2018), 1418-1425, 2018, Peer-reviwed
International conference proceedings, English - Classifier Generalization for Comprehensive Classifiers Subsumption in XCS
Caili Zhang; Takato Tatsumi; Hiroyuki Sato; Tim Kovacs; Keiki Takadama
Proc. of Evolutionary Computation in Health care and Nursing System, in Genetic and Evolutionary Computation Conference (GECCO 2018), 1854-1861, 2018, Peer-reviwed
International conference proceedings, English - Exploring Tradeoff Between Distance-minimality and Diversity of Landing Routes for Aircraft Landing Optimization
Akinori Murata; Hiroyuki Sato; Keiki Takadama
SICE Journal of Control, Measurement, and System Integration (JCMSI), 11, 5, 409-418, 2018, Peer-reviwed
Scientific journal, English - Toward Adaptation to Various Landscape Environment by Artificial Bee Colony Algorithm Based on Local Information Sharing
Ryo Takano; Hiroyuki Sato; Keiki Takadama
The 2nd International Symposium on Swarm Behavior and Bio-Inspired Robotics (SWARM 2017),, 147-154, 2017, Peer-reviwed
International conference proceedings, English - Applying Variance-based Learning Classifier System without Convergence of Reward Estimation into Various Reward Distribution
Takato Tatsumi; Hiroyuki Sato; Tim Kovacs; Keiki Takadama
2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2017, CEC, 2630-2637, 2017, Peer-reviwed, This paper focuses on a generalization of classifiers in noisy problems and aims at exploring learning classifier systems (LCSs) that can evolve accurately generalized classifiers as an optimal solution in several environments which include different type of noise. For this purpose, this paper employs XCS-CRE (XCS without Convergence of Reward Estimation) which can correctly identify classifiers as either accurate or inaccurate ones even in a noisy problem, and investigates its effectiveness in several noisy problems. Through intensive experiments of three LCSs (i.e., XCS as the conventional LCS, XCS-SAC (XCS with Self-adaptive Accuracy Criterion) as our previous LCS, and XCS-CRE) on the noisy 11-multiplexer problem where reward value changes according to (a) Gaussian distribution, (b) Cauchy distribution, or (c) Lognormal distribution, the following implications have been revealed: (1) the correct rate of the classifier of XCS-CRE and XCS-SAC converge to 100% in all three types of the reward distribution while that of XCS cannot reach 100%
(2) the population size of XCS-CRE is smallest followed by that of XCS-SAC and XCS
and (3) the percentage of the acquired optimal classifiers of XCS-CRE is highest followed by that of XCS-SAC and XCS.
International conference proceedings, English - Automatic Adjustment of Selection Pressure Based on Range of Reward in Learning Classifier System.
Takato Tatsumi; Hiroyuki Sato; Keiki Takadama
Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2017, Berlin, Germany, July 15-19, 2017, ACM, 505-512, 2017, Peer-reviwed
International conference proceedings - XCSR Learning from Compressed Data Acquired by Deep Neural Network
Kazuma Matsumoto; Takato Tatsumi; Hiroyuki Sato; Tim Kovacs; Keiki Takadama
Journal of Advanced Computational Intelligence and Intelligent Informatics (JACIII), 21, 5, 856-867, 2017, Peer-reviwed
Scientific journal, English - Learning Classifier System Based on Mean of Reward
Takato Tatsumi; Hiroyuki Sato; Keiki Takadama
Journal of Advanced Computational Intelligence and Intelligent Informatics (JACIII), 21, 5, 895-906, 2017, Peer-reviwed
Scientific journal, English - Strategies to Improve Cuckoo Search Toward Adapting Randomly Changing Environment.
Yuta Umenai; Fumito Uwano; Hiroyuki Sato; Keiki Takadama
Advances in Swarm Intelligence - 8th International Conference, ICSI 2017, Fukuoka, Japan, July 27 - August 1, 2017, Proceedings, Part I, Springer, 573-582, 2017, Peer-reviwed
International conference proceedings - Affinity Based Search Amount Control in Decomposition Based Evolutionary Multi-Objective Optimization
Hiroyuki Sato; Minami Miyakawa; Keiki Takadama
10th EAI International Conference on Bio-inspired Information and Communications Technologies (BICT), in USB memory, 8 pages, 1-8, 2017, Peer-reviwed
International conference proceedings, English - Polynomial Mean-Centric Crossover for Directed Mating in Evolutionary Constrained Multi-Objective Continuous Optimization
Minami Miyakawa; Hiroyuki Sato; Yuji Sato
10th EAI International Conference on Bio-inspired Information and Communications Technologies (BICT), in USB memory, 8 pages, 1-8, 2017, Peer-reviwed
International conference proceedings, English - An Improved MOEA/D Utilizing Variation Angles for Multi-Objective Optimization
Hiroyuki Sato; Minami Miyakawa; Keiki Takadama
Proc. of the 2017 Genetic and Evolutionary Computation Conference (GECCO 2017), 163-164, 2017, Peer-reviwed
International conference proceedings, English - Utilization of Infeasible Solutions in MOEA/D for Solving Constrained Many-objective Optimization Problems
Minami Miyakawa; Yuji Sato; Hiroyuki Sato
Proc. of Late-Breaking Abstracts at the 2017 Genetic and Evolutionary Computation Conference (GECCO 2017), 35-36, 2017, Peer-reviwed
International conference proceedings, English - 進化型多数目的最適化の現状と課題
佐藤寛之; 石渕久生
特集 多目的意思決定の深化と応用，オペレーションズ・リサーチ, 日本オペレーションズ・リサーチ学会 ; 1956-, 60, 3, 156-163, 2017, Peer-reviwed
Scientific journal, Japanese - A Study on Two-Level Infeasible Solution Repair for Evolutionary Multi-objective Set Packing Optimization
Mariko Tanaka; Yuki Yamagishi; Hidetoshi Nagai; Hiroyuki Sato
The 2017 International Symposium on Nonlinear Theory and Its Applications (NOLTA2017), 588-591, 2017, Peer-reviwed
International conference proceedings, English - A Study on Synapse Update of Inactive Cells in Cortical Learning Algorithm
Takeru Aoki; Keiki Takadama; Hiroyuki Sato
The 2017 International Symposium on Nonlinear Theory and Its Applications (NOLTA2017), 391-394, 2017, Peer-reviwed
International conference proceedings, English - Multi-Objective Optimization Problem Mapping Based on Algorithmic Parameter Rankings
Motoaki Kakuguchi; Minami Miyakawa; Keiki Takadama; Hiroyuki Sato
The 2017 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2017), 2809-2816, 2017, Peer-reviwed
International conference proceedings, English - Preventing Incorrect Opinion Sharing with Weighted Relationship Among Agents
Rei Saito; Masaya Nakata; Hiroyuki Sato; Tim Kovacs; Keiki Takadama
Human Interface and the Management of Information: Applications and Services, Pt II, SPRINGER INT PUBLISHING AG, 9735, 50-62, 2016, Peer-reviwed, This paper aims at investigating how correct or incorrect opinions are shared among the agents in the weighted network where the relationship among the agent (as nodes of its network) is different each other, and exploring how the agents can be promoted to share only correct opinions by preventing to acquire the incorrect opinions in the weighted network. For this purpose, this paper focuses on Autonomous Adaptive Tuning algorithm (AAT) which can improve an accuracy of correct opinion shared among agents in the various network, and improves it to address the situation which is close in the real world, i.e., the relationship among agents is different each other. This is because the original AAT does not consider such a different relationship among the agents. Through the intensive empirical experiments, the following implications have been revealed: (1) the accuracy of the correct opinion sharing with the improved AAT is higher than that with the original AAT in the weighted network; (2) the agents in the improved AAT can prevent to acquire incorrect opinion sharing in the weighted network, while those in the original AAT are hard to prevent in the same network.
International conference proceedings, English - Personalized Real-Time Sleep Stage from Past Sleep Data to Today's Sleep Estimation
Yusuke Tajima; Tomohiro Harada; Hiroyuki Sato; Keiki Takadama
Human Interface and the Management of Information: Applications and Services, Pt II, SPRINGER INT PUBLISHING AG, 9735, 501-510, 2016, Peer-reviwed, This paper focuses on the real-time sleep stage estimation and proposes the method which appropriately selects the past sleep data as the prior knowledge for improving accuracy of the sleep stage estimation. The prior knowledge in this paper is represented as the parameters for estimating the sleep stage and it is composed of 26 parameters which give an influence to the accuracy of the real-time sleep stage estimation. Concretely, these parameters are acquired from the heartbeat data of a certain past day, and they are used to estimate the heartbeat data of a current day, which data is finally converted to the sleep stage. The role of the proposed method is to select the appropriate parameters of the heartbeat data of a certain past day, which is similar to the heartbeat data of a current day. To investigate the effectiveness of the proposed method, we conducted the human subject experiment which investigated the accuracy of the real-time sleep stage estimation of two adult males ( whose age are 20 and 40) and one adult female ( whose age is 60) by employing the appropriate parameters of the different day from three days. The experimental results revealed that the accuracy of the real-time sleep stage estimation with the proposed method is higher than that without it.
International conference proceedings, English - Optimization of Aircraft Landing Route and Order Based on Novelty Search
Akinori Murata; Hiroyuki Sato; Keiki Takadama
INTELLIGENT AND EVOLUTIONARY SYSTEMS, IES 2016, SPRINGER INT PUBLISHING AG, 8, 291-304, 2016, Peer-reviwed, This paper focuses on the Aircraft Landing Problem (ALP) and proposes the efficient aircraft landing route and order optimization method compared to the conventional method. As a difficulty in solving ALP, both landing route and order of all aircrafts should be optimized together, meaning that they cannot be optimized independently. To tackle this problem, our method employs novelty search to generate variety candidates of aircraft landing routes, which are indispensable to generate the feasible landing order of all aircraft. Through the experiment on a benchmark problem, it has revealed that the proposed method can reduce the occupancy time of aircrafts in an airport.
International conference proceedings, English - Extracting Different Abstracted Level Rule with Variance-Based LCS
Caili Zhang; Takato Tatsumi; Masaya Nakata; Keiki Takadama; Hiroyuki Sato; Tim Kovacs
2016 JOINT 8TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING AND INTELLIGENT SYSTEMS (SCIS) AND 17TH INTERNATIONAL SYMPOSIUM ON ADVANCED INTELLIGENT SYSTEMS (ISIS), IEEE, 160-165, 2016, Peer-reviwed, This paper extends the variance-based Learning Classifier System (LCS) [4] called XCS-SAC [10], in order to extract two different abstracted level rules (i.e., classifiers). Since XCS-SAC attempts to evolve classifiers whose generality depends on their own parameter, such an attempt results in generating many specific classifiers (i.e., the classifiers having a less number of '#'). Due to inappropriate generalization, some of classifiers might not be human-understandable. To overcome this problem, our LCS focuses on an extraction of only two different abstracted level rules, both the specific and general rules, to understand a tendency in a given problem. In detail, the specific rules can be only utilized in limited situations but they are very accurate, while the general rules can be widely utilized but they are not accurate. The experimental result shows that our LCS succeeds to extract both specific and general rules appropriately in comparison with XCS-SAC.
International conference proceedings, English - Learning Classifier System with Deep Autoencoder
Kazuma Matsumoto; Rei Saito; Yusuke Tajima; Masaya Nakata; Hiroyuki Sato; Tim Kovacs; Keiki Takadama
2016 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), IEEE, 4739-4746, 2016, Peer-reviwed, This paper proposes a novel Learning Classifier System (LCS) which integrates Deep AutoEncoder named DAE to solve high-dimensional problems. In the proposed LCS, DAE starts to compress (encode) an environmental input as a high-dimensional information to an input of LCS as a low-dimensional information and decompresses (decodes) an output of LCS as a low-dimensional information to a system output as a high-dimensional information. Since the compressed inputs are encoded by real value, this paper employs XCSR (i.e., an LCS with real value coding) and combines XCSR with DAE. In order to investigate the effectiveness of the proposed LCS, XCSR with DAE, this paper conducts the preliminary experiment on the benchmark classification problem, i.e., 6-Multiplexer problem. The intensive experiments on the compression from 6 to 5 bits have revealed the following implications: (1) XCSR with DAE performs as well as XCSR even learning from the compressed input data; and (2) XCSR with DAE successfully decodes the compressed rules to extract the rules which are the same as those learned with not compressed input data.
International conference proceedings, English - Variance-based Learning Classifier System without Convergence of Reward Estimation
Takato Tatsumi; Takahiro Komine; Masaya Nakata; Hiroyuki Sato; Tim Kovacs; Keiki Takadama
PROCEEDINGS OF THE 2016 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO'16 COMPANION), ASSOC COMPUTING MACHINERY, 67-68, 2016, Peer-reviwed
International conference proceedings, English - A Modified Cuckoo Search Algorithm for Dynamic Optimization Problems
Yuta Umenai; Fumito Uwano; Yusuke Tajima; Masaya Nakata; Hiroyuki Sato; Keiki Takadama
2016 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), IEEE, 1757-1764, 2016, Peer-reviwed, This paper proposes a simple modification of the Cuckoo Search called CS for a dynamic environment. In this paper, we consider a dynamic optimization problem where the global optimum can be cyclically changed depending on time. Our modified CS algorithm holds good candidates in order to effectively explore the search space near those candidates with an intensive local search. Our first experiment tests the prosed method on a set of static optimization problems, which aims at evaluating the potential performance of the proposed method. Then, we apply it to a dynamic optimization problem. Experimental results on the static problems show that the proposed method derives a better performance than the conventional method, which suggest the proposed method potentially has a good capability of finding a good solution. On the dynamic problem, the proposed method also performs well while the conventional method fails to find a better solution.
International conference proceedings, English - Promoting Machine-code Program Evolution in Asynchronous Genetic Programming
Tomohiro Harada; Keiki Takadama; Hiroyuki Sato
SICE Journal of Control, Measurement, and System Integration (JCMSI), 9, 2, 93-102, 2016, Peer-reviwed
Scientific journal, English - Optimization of Aircraft Landing Route and Order: An Approach of Hierarchical Evolutionary Computation
Akinori Murata; Masaya Nakata; Hiroyuki Sato; Tim Kovacs; Keiki Takadama
EAI Endorsed Transactions on Self-Adaptive Systems, 2, 6, 1-8, 2016, Peer-reviwed
Scientific journal, English - Analysis of Inverted PBI and Comparison with Other Scalarizing Functions in Decomposition Based MOEAs
Hiroyuki Sato
JOURNAL OF HEURISTICS, SPRINGER, 21, 6, 819-849, 2016, Peer-reviwed, MOEA/D is one of the promising evolutionary approaches for solving multi and many-objective optimization problems. MOEA/D decomposes a multi-objective optimization problem into a number of single objective optimization problems. Each single objective optimization problem is defined by a scalarizing function using a weight vector. InMOEA/D, there are several scalarizing approaches such as weighted Tchebycheff, reciprocal weighted Tchebycheff, weighted sum (WS) and penalty-based boundary intersection (PBI). Each scalarizing function has a characteristic effect on the search performance of MOEA/D and provides a scenario of multi-objective solution search. To improve the availability of MOEA/D framework for solving various kinds of problems, it is important to provide a new scalarizing function which has different characteristics from the conventional scalarizing functions. In particular, the conventional scalarizing approaches face a difficulty to approximate a widely spread Pareto front in some problems. To approximate the entire Pareto front by improving the spread of solutions in the objective space and enhance the search performance of MOEA/D in multi and many-objective optimization problems, in this work we propose the inverted PBI scalarizing approach which is an extension of the conventional PBI and WS. In this work, we analyze differences between inverted PBI and other scalarizing functions, and compare the search performances of NSGA-III and five MOEA/Ds using weighted Tchebycheff, reciprocal weighted Tchebycheff, WS, PBI and inverted PBI in many-objective knapsack problems and WFG4 problems with 2-8 objectives. As results, we show that the inverted PBI based MOEA/D achieves higher search performance than other algorithms in problems with many-objectives and the difficulty to approximate a widely spread Pareto front in the objective space. Also, we show the robustness of the inverted PBI on Pareto front geometry by using problems with four representative concave, linear, convex and discontinuous Pareto fronts.
Scientific journal, English - Controlling Selection Areas of Useful Infeasible Solutions for Directed Mating in Evolutionary Constrained Multi-objective Optimization
Minami Miyakawa; Keiki Takadama; Hiroyuki Sato
ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE, SPRINGER, 76, 45293, 25-46, 2016, Peer-reviwed, As an evolutionary approach to solve constrained multi-objective optimization problems (CMOPs), recently an algorithm using the two-stage non-dominated sorting and the directed mating (TNSDM) was proposed. In TNSDM, the directed mating utilizes infeasible solutions dominating feasible solutions in the objective space to generate offspring. The directed mating significantly contributes to the search performance improvement in evolutionary constrained multi-objective optimization. However, the conventional directed mating has two problems. First, since the conventional directed mating selects a pair of parents based on the conventional Pareto dominance, two parents having different search directions may be mated. Second, the directed mating cannot be performed in some cases especially when the population has few useful infeasible solutions. In this case, the conventional mating using only feasible solutions is performed instead. Thus, the effectiveness of the directed mating cannot always be achieved depending on the number of useful infeasible solutions. To overcome these problems and further enhance the effect of the directed mating in TNSDM, in this work we propose a method to control the selection area of useful infeasible solutions by controlling dominance area of solutions (CDAS). We verify the effectiveness of the proposed method in TNSDM, and compare its search performance with the conventional CNSGA-II on discrete m-objective k-knapsack problems and continuous mCDTLZ problems. The experimental results show that the search performance of TNSDM is further improved by controlling the selection area of useful infeasible solutions in the directed mating.
Scientific journal, English - Evolutionary Multi-Level Robust Solution Search for Noisy Multi-Objective Optimization Problems with Different Noise Levels
Hiroyuki Sato; Tomohisa Hashimoto
International Journal of Automation and Logistics (IJAL), 2, 45293, 4-25, 2016, Peer-reviwed
Scientific journal, English - Evolutionary Multi-Objective Route and Fleet Assignment Optimization for Regular and Non-Regular Flights
Keiki Takadama; Takahiro Jinba; Tomohiro Harada; Hiroyuki Sato
International Journal of Automation and Logistics (IJAL), 2, 45293, 122-152, 2016, Peer-reviwed
Scientific journal, English - Chain-reaction Solution Update in MOEA/D and Its Effects on Multi- and Many-objective Optimization
Hiroyuki Sato
SOFT COMPUTING, SPRINGER, 20, 10, 3803-3820, 2016, Peer-reviwed, MOEA/D is one of the promising evolutionary algorithms for multi- and many-objective optimization. To improve the search performance of MOEA/D, this work focuses on the solution update method in the conventional MOEA/D and proposes its alternative, the chain-reaction solution update. The proposed method is designed to maintain and improve the variable (genetic) diversity in the population by avoiding duplication of solutions in the population. In addition, the proposed method determines the order of existing solutions to be updated depending on the location of each offspring in the objective space. Furthermore, when an existing solution in the population is replaced by a new offspring, the proposed method tries to reutilize the existing solution for other search directions by recursively performing the proposed chain-reaction update procedure. This work uses discrete knapsack and continuous WFG4 problems with 2-8 objectives. Experimental results using knapsack problems show the proposed chain-reaction update contributes to improving the search performance of MOEA/D by enhancing the diversity of solutions in the objective space. In addition, experimental results using WFG4 problems show that the search performance of MOEA/D can be further improved using the proposed method.
Scientific journal, English - A Study on Directional Repair of Infeasible Solutions for Multi-Objective Knapsack Problems
Minami Miyakwa; Keiki Takadama; Hiroyuki Sato
The 2016 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP2016), 225-228, 2016, Peer-reviwed
International conference proceedings, English - A Study on Evolutionary Multi-level Robust Solution Search for Multi-objective Optimization Involving Multi-dimensional Noise
Tomohisa Hashimoto; Minami Miyakwa; Keiki Takadama; Hiroyuki Sato
The 2016 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP2016), 229-232, 2016, Peer-reviwed
International conference proceedings, English - Enhanced Decomposition-Based Many-Objective Optimization Using Supplemental Weight Vectors
Hiroyuki Sato; Satoshi Nakagawa; Minami Miyakawa; Keiki Takadama
2016 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), IEEE, 1626-1633, 2016, Peer-reviwed, In evolutionary multi-objective optimization, each solution in the population generally has two roles. The first one is to approximate a part of the Pareto front, and the second one is to be a variable information resource to generate offspring. In many-objective optimization involving four or more conflicting objectives, solutions in the population have to be sparsely distributed in the objective space and the variable space to approximate a high-dimensional Pareto front, and each solution faces the difficulty to play the second role since variables are drastically individualized in the population. To overcome this problem, we focus on MOEA/D algorithm framework and propose a method to introduce supplemental weight vectors and solutions which maintain variable information resource to enhance the solution search for each part of the Pareto front. Experimental results using many-objective knapsack problems show that the supplemental weight vectors and solutions improves the search performance of MOEA/D by improving the diversity of the obtained solutions.
International conference proceedings, English - Directional Repair in Evolutionary Optimization of m-Objective k-Knapsack Problems
Minami Miyakawa; Keiki Takadama; Hiroyuki Sato
Journal of Signal Processing, Research Institute of Signal Processing, Japan, 20, 4, 161-164, 2016, Peer-reviwed, To solve m-objective k-knapsack problems (mk-KPs) by using evolutionary algorithms, we propose a repair method that transforms infeasible solutions into feasible ones. In evolutionary multi-objective optimization, each solution in the population has a role in approximating a part of the Pareto front. However, since the conventional weighted scalar repair method (WSR) does not consider the position of each solution in the objective space, the solution diversity to approximate a wide range of the Pareto front is deteriorated. To improve the search performance of evolutionary algorithms for solving mk-KPs by enhancing the diversity of solutions, we propose a repair method considering the positions and repair directions of infeasible solutions in the objective space. Experimental results show that the proposed method improves the diversity of solutions and achieves higher search performance than the conventional WSR in mk-KPs.
Scientific journal, English - Evolutionary Algorithmic Parameter Optimization of MOEAs for Multiple Multi-Objective Problems
Motoaki Kakuguchi; Minami Miyakawa; Keiki Takadama; Hiroyuki Sato
2016 JOINT 8TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING AND INTELLIGENT SYSTEMS (SCIS) AND 17TH INTERNATIONAL SYMPOSIUM ON ADVANCED INTELLIGENT SYSTEMS (ISIS), IEEE, 30-35, 2016, Peer-reviwed, Nowadays, algorithmic studies of multi-objective evolutionary algorithms (MOEAs) are flooded with too many search algorithms. Each MOEA has its own expert problem domain. To clarify not only the optimal MOEA and its parameters for each of multiple multi-objective optimization problems (MOPs) but the robust MOEAs for multiple MOPs, this work proposes a meta-MOEA framework to search the Pareto optimal algorithmic parameters for multiple MOPs. In this work, we use two DTLZ2 benchmark problems with 2 and 4 objectives and optimize the base algorithm, the crossover rate and its parameter, the mutation rate and its parameter for the both DTLZ2 problems by the meta-MOEA. The experiment results show that the optimal algorithmic parameters for each of two DTLZ2 problems are different and the robust algorithmic parameters for both problems can be obtained by the meta-MOEA framework.
International conference proceedings, English - Effects of Chain-Reaction Initial Solution Arrangement in Decomposition-Based MOEAs
Hiroyuki Sato; Minami Miyakawa; Keiki Takadama
The 9th Eurosim Congress on Modelling and Simulation (EUROSIM2016), 992-997, 2016, Peer-reviwed
International conference proceedings, English - 進化計算による多数目的最適化
佐藤寛之
「進化計算の新時代」特集号，システム制御情報学会論文誌，システム/制御/情報, 60, 7, 265-271, 2016, Peer-reviwed
Scientific journal, Japanese - Estimating Surrounding Symptom Level of Dementia Patient by Sleep Stage
Shingo Tomura; Tomohiro Harada; Hiroyuki Sato; Keiki Takadama; Makoto Aoki
2015 9TH INTERNATIONAL SYMPOSIUM ON MEDICAL INFORMATION AND COMMUNICATION TECHNOLOGY (ISMICT), IEEE, 190-194, 2015, Peer-reviwed, This paper explores the methods that can estimate a surrounding symptom level of dementia patients by monitoring the sleep features such as the sleep rhythm (cycle) and the light/deep sleep ratio. For this purpose, we employ Watanabe's method to estimate the sleep stages as the sleep feature, which can be transformed from the heartbeat data acquired by the mattress-based sensor. To explore the dementia estimation methods, we conducted the human subject experiment in the hospital. The analysis of the sleep stage of the dementia patient who has the light cognitive function impairment with repeating good and bad healthy conditions have revealed that the averaged cycles of the REM sleep and the light/deep sleep ratio have a great potential of the evaluation criteria that can estimate a surrounding symptom level of dementia patients.
International conference proceedings, English - Optimization of Aircraft Landing Route and Order: An Approach of Hierarchical Evolutionary Computation.
Akinori Murata; Masaya Nakata; Hiroyuki Sato; Tim Kovacs; Keiki Takadama
BICT 2015, Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS), New York City, United States, December 3-5, 2015, ICST/ACM, 340-347, 2015, Peer-reviwed
International conference proceedings - Adaptive Control of Dominance Area of Solutions in Evolutionary Many-objective Optimization
Kouhei Tomita; Minami Miyakawa; Hiroyuki Sato
New Mathematics and Natural Computation, World Scientific Publishing Co. Pte Ltd, 11, 2, 135-150, 2015, Peer-reviwed, Controlling the dominance area of solutions (CDAS) relaxes the concept of Pareto dominance with an user-defined parameter S. CDAS with S <
0.5 expands the dominance area and improves the search performance of multi-objective evolutionary algorithms (MOEAs) especially in many-objective optimization problems (MaOPs) by enhancing convergence of solutions toward the optimal Pareto front. However, there is a problem that CDAS with an expanded dominance area (S <
0.5) generally cannot approximate entire Pareto front. To overcome this problem we propose an adaptive CDAS (A-CDAS) that adaptively controls the dominance area of solutions during the solutions search. Our method improves the search performance in MaOPs by approximating the entire Pareto front while keeping high convergence. In early generations, A-CDAS tries to converge solutions toward the optimal Pareto front by using an expanded dominance area with S <
0.5. When we detect convergence of solutions, we gradually increase S and contract the dominance area of solutions to obtain Pareto optimal solutions (POS) covering the entire optimal Pareto front. We verify the effectiveness and the search performance of the proposed A-CDAS on concave and convex DTLZ3 benchmark problems with 2-8 objectives, and show that the proposed A-CDAS achieves higher search performance than conventional non-dominated sorting genetic algorithm II (NSGA-II) and CDAS with an expanded dominance area.
Scientific journal, English - MOEA/D Using Constant-Distance Based Neighbors Designed for Many-Objective Optimization
Hiroyuki Sato
2015 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), IEEE, 2867-2874, 2015, Peer-reviwed, Several recent studies showed the effectiveness of MOEA/D for many-objective optimization. However, MOEA/D was originally proposed not for many-objective optimization but for multi-objective optimization. Therefore, its algorithm causes several problems in many-objective optimization. MOEA/D uses the neighbor mating, and neighbors are determined by the user-defined neighbors' size T. For each weight vector which determines a search direction in the objective space, MOEA/D calculates distances to all weights and find T nearest weights as its neighbors. However, the number of weights having the same distance is increased as the number of objectives is increased, and MOEA/D faces the difficulty to determine neighbors by the neighbors' size T in many-objective optimization. Also, especially for the extreme weights to search the extreme objective function values, weights far from them are included as their neighbors. It causes a negative effect on the search of the extreme objective values. To overcome these problems and enhance the search performance of MOEA/D by improving its algorithm appropriately for many-objective optimization, in this work we focus on the handling of neighbors and propose an improved MOEA/D including the constant-distance based neighbors and the tournament selection based on the scalarizing function values. We use many-objective knapsack problems with 2-8 objectives and compare the search performances of the conventional MOEA/D, the improved MOEA/D and NSGA-III. As the results, we show that the improved MOEA/D achieves higher search performance than the conventional MOEA/D and NSGA-III by improving the diversity of the obtained solutions in the objective space.
International conference proceedings, English - Directed Mating Using Inverted PBI Function for Constrained Multi-Objective Optimization
Minami Miyakawa; Keiki Takadama; Hiroyuki Sato
2015 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), IEEE, 2929-2936, 2015, Peer-reviwed, In evolutionary constrained multi-objective optimization, the directed mating utilizing useful infeasible solutions having better objective function values than feasible solutions significantly contributes to improving the search performance. This work tries to further improve the effectiveness of the directed mating by focusing on the search directions in the objective space. Since the conventional directed mating picks useful infeasible solutions based on Pareto dominance, all solutions are given the same search direction regardless of their locations in the objective space. To improve the diversity of the obtained solutions in evolutionary constrained multi-objective optimization, we propose a variant of the directed mating using the inverted PBI (IPBI) scalarizing function. The proposed IPBI-based directed mating gives unique search directions to all solutions depending on their locations in the objective space. Also, the proposed IPBI-based directed mating can control the strength of directionality for each solution's search direction by the parameter theta. We use discrete m-objective k-knapsack problems and continuous mCDTLZ problems with 2-4 objectives and compare the search performances of TNSDM algorithm using the conventional directed mating and the proposed TNSDM-IPBI using IPBI-based directed mating. The experimental results shows that the proposed TNSDM- IPBI using the appropriate theta* achieves higher search performance than the conventional TNSDM in all test problems used in this work by improving the diversity of solutions in the objective space.
International conference proceedings, English - An Efficient Parallel Solution Evaluations in MOEA/D by Avoiding Overlaps of Neighbors
Hiroyuki Sato; Minami Miyakawa; Elizabeth Perez-Cortes
Proc. of Workshop on Evolutionary Multi-Objective Optimization at 2015 IEEE Congress on Evolutionary Computation (CEC2015), 11-16, 2015, Peer-reviwed
International conference proceedings, English - A Study on Gradual Enhancement of the Approximation Granularity of Pareto Front in Evolutionary Many-Objective Optimization
Tomohisa Hashimoto; Hiroyuki Sato; Minami Miyakawa
Proc. of Workshop on Evolutionary Multi-Objective Optimization at 2015 IEEE Congress on Evolutionary Computation (CEC2015), 17-22, 2015, Peer-reviwed
International conference proceedings, English - A Parallel MOEA/D Generating Solutions in Minimum Overlapped Update Ranges of Solutions
Hiroyuki Sato; Minami Miyakawa; Elizabeth Pérez-Cortés
GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference, Association for Computing Machinery, Inc, 775-776, 2015, Peer-reviwed, This paper proposes a parallel MOEA/D which assigns the computational resources to generate solutions in the minimum overlapped update ranges of solutions. The search performance is verifie on DTLZ2 problem and a car design optimization using TORCS.
International conference proceedings, English - Control of Crossed Genes Ratio for Directed Mating in Evolutionary Constrained Multi-Objective Continuous Optimization
Minami Miyakawa; Keiki Takadama; Hiroyuki Sato
GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference, Association for Computing Machinery, Inc, 1201-1204, 2015, Peer-reviwed, As an evolutionary approach to solve constrained multi-objective optimization problems (CMOPs), an algorithm using the two-stage non-dominated sorting and the directed mating (TNSDM) has been proposed. To generate offspring, the directed mating utilizes useful infeasible solutions having better objective values than feasible solutions in the population. The directed mating achieves higher search performance than the conventional mating which avoids using infeasible solutions in several CMOPs. However, since the directed mating uses infeasible solutions, generated offspring tend to be infeasible compared with the conventional mating. To further improve the effectiveness of the directed mating by improving the feasibility of generated offspring, in this work we propose a method to control the crossed genes ratio in the directed mating. In this method, we control the amount of genes copied from infeasible parents to offspring in the directed mating. Experimental results using m-objective k-knapsack problem with 2-4 objectives show the contribution of the directed mating for the search performance is further improved by controlling crossed genes ratio.
International conference proceedings, English - Evolutionary Algorithms for Uncertain Evaluation Functions
Yusuke Tajima; Masaya Nakata; Hiroyasu Matsushima; Yoshihiro Ichikawa; Hiroyuki Sato; Kiyohiko Hattori; Keiki Takadama
New Mathematics and Natural Computation, WORLD SCIENTIFIC PUBL CO PTE LTD, 11, 2, 201-215, 2015, Peer-reviwed, This paper proposes the evolutionary algorithm (EA) for the uncertain evaluation function in which fitness values change even with the same input. In detail, the proposed method employs the probability model to acquire the appropriate attributes that can drive the good solutions. To investigate the effectiveness of the proposed method, we apply it to sleep stage estimation problem where an accuracy of sleep stage estimation changes even in the same estimation filter (correspondingly the solutions). The experimental results have revealed the following implications: (i) The proposed method succeeded to acquire the robust estimation filters which stably derive a high accuracy of the sleep stage estimation; (ii) in detail, the proposed method with the roulette selection shows higher performance than the one with the random selection; and (iii) the proposed method shows high performance and robustness to the different days in comparison with the conventional sleep stage estimation method.
Scientific journal, English - Multi-Objective Optimization for Common and Special Components: First Step Toward Network Optimization of Regular and Non-Regular Flights
Takahiro Jinba; Hiroto Kitagawa; Eriko Azuma; Keiji Sato; Hiroyuki Sato; Kiyohiko Hattori; Keiki Takadama
New Mathematics and Natural Computation, World Scientific Publishing Co. Pte Ltd, 11, 2, 183-199, 2015, Peer-reviwed, To optimize the problem composed of (i) the common components which should be optimized from the viewpoint of all objective functions and (ii) the special components which should be optimized from the viewpoint of one of the objective functions, this paper proposes a new multi-objective optimization method which optimizes not only the common components for all objective functions but also the special ones for each objective function. To investigate the effectiveness of the proposed method, this paper tested our method on the test-bed problem which is an extended version of the 0/1 knapsack problem. The intensive experiments have revealed the following implications: (i) Our method finds better solutions which have higher fitness than the conventional method (NSGA-II)
(ii) our method can find the solutions that had a large norm (which corresponds to a high profit of an airline company in the flight scheduling problem) with the high rate of the common components
and (iii) since the crowding distance employed in our method contributes to keeping the diversity during the solution search, our method has high exploration capability of solutions.
Scientific journal, English - 許容誤差を自己適応可能な学習分類子システム
辰巳 嵩豊; 小峯 嵩裕; 中田 雅也; 佐藤 寛之; 髙玉 圭樹
進化計算学会論文誌, The Japanese Society for Evolutionary Computation, 6, 2, 90-103, 2015, Peer-reviwed, The XCS classifier system is designed to evolve accurately generalized classifiers as an optimal solution to a problem. All classifiers are identified as either accurate or inaccurate on the basis of a pre-defined parameter called an accuracy criterion. Previous results suggested a standard setting of the accuracy criterion robustly performs on multiple simple problems so XCS evolves the optimal solution. However, there lacks a guideline of reasonable setting of accuracy criterion. This causes a problem that the accuracy criterion should be empirically customized for each complex problems especially noisy problems which is a main focus of this paper. This paper proposes a self-adaptation technique for the accuracy criterion which attempts to enable XCS to evolve the optimal solution on the noisy problems. In XCS-SAC(XCS with Self-Adaptive accuracy criterion), each classifier has its own accuracy criterion in order to find an adequate setting of accuracy criterion for each niche. Then, each classifier's accuracy criterion is updated with the variance of reward which its classifier has received. We test XCS-SAC on a benchmark classification problem (i.e., the multiplexer problem) with noise (the Gaussian noise and alternative noise). Experimental results show XCS-SAC successfully solves the noisy multiplexer problems as well as XCS but evolves a more compact solution including an optimal solution than XCS.
Scientific journal, Japanese - Estimating Surrounding Symptom Level of Dementia Person by Sleep Stage
Shingo Tomura; Tomohiro Harada; Hiroyuki Sato; Keiki Takadama; Makoto Aoki
The Ninth International Symposium on Medical Information and Communication Technology (ISMICT 2015), 201-205, 2015, Peer-reviwed
International conference proceedings, English - Handling Different Level of Unstable Reward Environment Through an Estimation of Reward Distribution in XCS
Takato Tatsumi; Takahiro Komine; Hiroyuki Sato; Keiki Takadama
2015 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), IEEE, 2973-2980, 2015, Peer-reviwed, XCS is an accuracy-based learning classifier system (LCS) which is powered by a reinforcement algorithm. We expect it will have when the reward for a state I action pair is unstable, because it is not possible to correctly estimate the evaluation. This paper focuses on learning in a different level of an unstable reward environment and proposes XCS-URE (XCS for Unstable Reward Environment) by improving XCS for such an environment. For this purpose, XCS-URE estimates the reward distribution of the classifier (i.e., if-then rule) by using the standard deviation of the acquired reward, and adjusts the accuracy of the classifier depending on the reward distribution. In order to investigate the effectiveness of XCS-URE, this paper applies XCS and XCS-URE into the multiple unstable reward environments which have a different level of the unstable rewards added by Gaussian noise. The experiments on the modified multiplexer problems have the following implications: (1) in the environment same Gaussian noise is added, XCS cannot performs properly due to the low accuracy of the classifier in the noisy environments, while XCS-URE can perform properly by acquiring the appropriate classifiers even in such an environment; (2) in the same environment, XCS-URE can reduce the population size without decreasing the correct rate as compared to XCS; and (3) even in the environment different Gaussian noises depending on the situation are added, XCS-URE can reduce the population size without decreasing the correct rate by adjusting the accuracy of the classifier depending on the reward distribution.
International conference proceedings, English - Toward Robustness Against Environmental Change Speed by Artificial Bee Colony Algorithm Based on Local Information Sharing
Ryo Takano; Tomohiro Harada; Hiroyuki Sato; Keiki Takadama
2015 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), IEEE, 1424-1431, 2015, Peer-reviwed, This paper focuses on Artificial Bee Colony (ABC) algorithm in multimodal problems with dynamic environmental change, and proposes the additional improvement of ABC algorithm based on local information sharing (ABC-lis) toward robustness against environmental change speed. The additional improvement is that scout bee's phase is modified to calculate by sigmoid function. To investigate the global search ability of ABC-lis and the additional improvement, we compare these algorithms to 3 case of environmental change speeds. The experimental result revealed that the following implications: (1) ABC-lis cannot always maintains the search capability in any change speed. (2) ABC-lis with the additional improvement is able to exert a high performance at every change speed. (3) The number of bees in each local area is able to be controlled by the novel parameter N-l in ABC-lis with the additional improvement.
International conference proceedings, English - Sightseeing Plan Recommendation System Using Sequential Pattern Mining Based on Adjacent Activities
Takuma Fujitsuke; Tomohiro Harada; Hiroyuki Sato; Keiki Takadam; Tomohiro Yamaguchi
2015 10TH ASIAN CONTROL CONFERENCE (ASCC), IEEE, 1-8, 2015, Peer-reviwed, This study proposes the novel sightseeing plan recommendation system based on the ad jacency of the activities in the plans (e.g., a shopping is selected after a lunch), and aims at verifying its effectiveness through the subject experiments. The proposed system recommends the sightseeing plans according to the adjacent relationship of the activities in the plans given by the group of other users whose preference are similar to the user. The intensive subject experiments have revealed that our proposed system can recommend the plans that contains the appropriate order of activities for the sightseeing plan recommendation in comparison with ones recommended by the conventional system.
International conference proceedings, English - Ship Route Evolutionary Optimization of Multiple Ship Companies for Distributed Coordination of Resources
Keiki Takadama; Eriko Azuma; Hiroyuki Sato; Takahiro Majima; Daisuke Watanabe; Mitujiro Katuhara
2015 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), IEEE, 1450-1457, 2015, Peer-reviwed, This paper proposes a ship route evolutionary optimization method for competitive ship companies in the industrial logistic network, where many alliances composed of several ship companies compete with others to acquire their resources (i.e., container) for maximizing their profits. One of the significant issues in the industrial logistic network is to find the best distribution of the resources in all alliances even in a competitive market. For this purpose, this paper explores the ship route optimization method for all competitive alliances, which can find their ship routes having higher profit than their actual routes through a good distributed coordination of resources. The intensive analysis of the results on the Pacific Ocean liner route with the actual data have revealed that the following implications: (1) even in competitive situation, the proposed evolutionary optimization method succeeds to find the ship routes of all alliances which can improve their own profits in comparison with those optimized by the conventional approach and those of actual routes, and (2) the ship routes generated by the proposed method have more anchor ports than the actual ship routes while keeping the ship constraints (e.g., the type of ships that each alliance has), which contributes to obtaining the appropriate resources as a good distributed coordination.
International conference proceedings, English - Control of Variable Exchange Probability for Directed Mating in Evolutionary Constrained Multi-Objective Continuous Optimization
Minami Miyakawa; Keiki Takadama; Hiroyuki Sato
2015 3RD INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL AND BUSINESS INTELLIGENCE (ISCBI 2015), IEEE, 33-38, 2015, Peer-reviwed, For solving constrained multi-objective optimization problems, an evolutionary algorithm using the two-stage non-dominated sorting and the directed mating (TNSDM) has been proposed. To generate offspring, the directed mating utilizes useful infeasible solutions having better objective values than feasible solutions in the population while conventional approaches avoid to use infeasible solutions as parents. Actually, the directed mating significantly contributes to improving the search performance of evolutionary constrained multi-objective optimization. To cross genes (variables) of selected parents, so far commonly-used crossover operators have been combined with the directed mating. To further improve the effectiveness of the directed mating in continuous problems, in this work we propose a method to control the amount of variables inherited from useful infeasible parents by varying the variable exchange probability in the SBX-based variation. Experimental results using two benchmark problems, TNK and mCDTLZ, with 2-4 objectives show that the effectiveness of the directed mating in continuous problems is further improved by increasing variables inherited from useful infeasible parents.
International conference proceedings, English - Preferred Region Based Evolutionary Multi-Objective Optimization Using Parallel Coordinates Interface
Hiroyuki Sato; Kouhei Tomita; Minami Miyakawa
2015 3RD INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL AND BUSINESS INTELLIGENCE (ISCBI 2015), IEEE, 89-94, 2015, Peer-reviwed, This work proposes a novel preference based evolutionary multi and many-objective optimization approach to search a specific region of the Pareto front. First, to know the overview of the entire Pareto front, the proposed approach roughly approximates it by using a representative MOEA/D with uniformly distributed weight vectors. Then, the obtained solutions are plotted on the parallel coordinates user interface (UI). In the proposed approach, the decision maker's preference can be specified as a region in the objective space while the conventional approaches use a single preference point in the objective space. It has an advantage when the decision maker has poor knowledge about the target problem. Next, the proposed approach rearranges the weight vectors to determine the search directions in the objective space inside the preferred region and performs MOEA/D with the rearranged weight vectors. The parallel coordinates UI is particularly suited to rearrange weight vectors and compatible with MOEA/D. Experimental results using DLTZ2 problems with 2-6 objectives show the proposed approach improves the approximation performance of the specific region of Pareto front.
International conference proceedings, English - Archive of Useful Solutions for Directed Mating in Evolutionary Constrained Multiobjective Optimization
Minami Miyakawa; Keiki Takadama; Hiroyuki Sato
The Journal of Advanced Computational Intelligence and Intelligent Informatics (JACIII), Fuji Technology Press, 18, 2, 221-231, 2014, Peer-reviwed
Scientific journal, English - Controlling Selection Area of Useful Infeasible Solutions in Directed Mating for Evolutionary Constrained Multiobjective Optimization
Minami Miyakawa; Keiki Takadama; Hiroyuki Sato
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Verlag, 8426, 137-152, 2014, Peer-reviwed, As an evolutionary approach to solve multi-objective optimization problems involving several constraints, recently a MOEA using the two-stage non-dominated sorting and the directed mating (TNSDM) has been proposed. In TNSDM, the directed mating utilizes infeasible solutions dominating feasible solutions in the objective space to generate offspring. Our previous work showed that the directed mating significantly contributed to improve the search performance of TNSDM on several benchmark problems. However, the conventional directed mating has two problems. First, since the conventional directed mating selects a pair of parents based on the conventional Pareto dominance, two parents having different search directions are mated in some cases. Second, in problems with high feasibility ratio, since the number of infeasible solutions in the population is low, sometimes the directed mating cannot be performed. Consequently, the effectiveness of the directed mating cannot be obtained. To overcome these problems and further improve the effectiveness of the directed mating in TNSDM, in this work we propose a method to control selection areas of infeasible solutions by controlling dominance area of solutions (CDAS). We verify the effectiveness of the proposed method in TNSDM, and compare its search performance with the conventional CNSGA-II on m objectives k knapsacks problems. As results, we show that the search performance of TNSDM is further improved by controlling selection area of infeasible solutions in the directed mating. © 2014 Springer International Publishing.
International conference proceedings, English - A Study on Distribution Control of Search Directions in Evolutionary Multi-Objective Optimization
Hiroyuki Sato
Proc. of the 2014 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP2014), 265-268, 2014, Peer-reviwed
International conference proceedings, English - Inverted PBI in MOEA/D and Its Impact on the Search Performance on Multi and Many-Objective Optimization
Hiroyuki Sato
GECCO'14: PROCEEDINGS OF THE 2014 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, ASSOC COMPUTING MACHINERY, 645-652, 2014, Peer-reviwed, MOEA/D decomposes a multi-objective optimization problem into a number of single objective optimization problems. Each single objective optimization problem is defined by a scalarizing function using a weight vector. In MOEA/D, there are several scalarizing approaches such as the weighted Tchebycheff, the weighted sum, and the PBI (penalty-based boundary intersection). However, these conventional scalarizing approaches face a difficulty to approximate a widely spread Pareto front in some problems. To enhance the spread of Pareto optimal solutions in the objective space and improve the search performance of MOEA/D especially in many-objective optimization problems, in this work we propose the inverted PBI scalarizing approach which is an extension of the conventional PBI. We use many-objective knapsack problems and WFG4 problems with 2-8 objectives, and compare the search performance of NSGA-III and four MOEA/Ds using the weighted Tchebycheff, the weighted sum, the PBI and the inverted PBI. As results, we show that MOEA/D using the inverted PBI achieves higher search performance than other algorithms in problems with many-objectives and the difficulty to obtain a widely spread Pareto front in the objective space.
International conference proceedings, English - Controlling Selection Area of Useful Infeasible Solutions and Their Archive for Directed Mating in Evolutionary Constrained Multiobjective Optimization
Minami Miyakawa; Keiki Takadama; Hiroyuki Sato
GECCO'14: PROCEEDINGS OF THE 2014 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, ASSOC COMPUTING MACHINERY, 629-636, 2014, Peer-reviwed, As an evolutionary approach to solve constrained multi-objective optimization problems (CMOPs), recently a MOEA using the two-stage non-dominated sorting and the directed mating (TNSDM) has been proposed. In TNSDM, the directed mating utilizes infeasible solutions dominating feasible solutions to generate offspring. Although the directed mating contributes to improve the search performance of TNSDM in CMOPs, there are two problems. First, since the number of infeasible solutions dominating feasible solutions in the population depends on each CMOP, the effectiveness of the directed mating also depends on each CMOP. Second, infeasible solutions utilized in the directed mating are discarded in the selection process of parents (elites) population and cannot be utilized in the next generation. To overcome these problems and further improve the effectiveness of the directed mating in TNSDM, in this work we propose an improved TNSDM introducing a method to control selection area of infeasible solutions and an archiving strategy of useful infeasible solutions for the directed mating. The experimental results on m objectives k knapsacks problems shows that the improved TNSDM improves the search performance by controlling the directionality of the directed mating and increasing the number of directed mating executions in the solution search.
International conference proceedings, English - A Study on Multi-level Robust Solution Search for Noisy Multi-objective Optimization Problems
Tomohisa Hashimoto; Hiroyuki Sato
PROCEEDINGS OF THE 18TH ASIA PACIFIC SYMPOSIUM ON INTELLIGENT AND EVOLUTIONARY SYSTEMS, VOL 2, SPRINGER INT PUBLISHING AG, 239-253, 2014, Peer-reviwed, For noisy multi-objective optimization problems involving multiple noisy objective functions, we aim to develop a two-stage multi-criteria decision-making system considering not only the objective values but also the noise level of each solution. In the first stage, the decision maker selects a solution with a preferred balance of objective values from the obtained Pareto optimal solutions without considering the noise level. In the second stage, for the preferred balance of objective values, this system shows several solutions with different levels of the noise and guides the decision-making considering the noise level of solutions. For the two-stage multi-criteria decision-making system, in this work we propose an algorithm to simultaneously find multi-level robust solutions with different noise levels for each search direction in the objective space. The experimental results using noisy DTLZ2 and multi-objective knapsack problems shows that the proposed algorithm is able to obtain multi-level robust solutions with different noise levels for each search direction in a single run of the algorithm.
International conference proceedings, English - Adaptive Update Range of Solutions in MOEA/D for Multi and Many-Objective Optimization
Hiroyuki Sato
SIMULATED EVOLUTION AND LEARNING (SEAL 2014), SPRINGER-VERLAG BERLIN, 8886, 274-286, 2014, Peer-reviwed, MOEA/D, a representative multi-objective evolutionary algorithm, decomposes a multi-objective optimization problem into a number of single objective optimization problems and tries to approximate Pareto front by simultaneously optimizing each of these single objective problems. MOEA/D has several options to calculate a scalar value from multiple objective function values of a solution. In many-objective optimization problems including four or more objective functions, MOEA/D using the weighted sum scalarizing function achieves high search performance. However, the weighted sum has a serious problem that the entire concave Pareto front cannot be approximated. To overcome this problem of the weighted sum based MOEA/D, in this work we propose a method to adaptively determine update ranges of solutions in the framework of MOEA/D. The experimental results show that the weighted sum based MOEA/D using the proposed solution update method can approximate the entire concave Pareto front and improve the search performance.
International conference proceedings, English - Multi Objective Optimization for Route Planning and Fleet Assignment in Regular and Non-regular Flights
Takahiro Jinba; Tomohiro Harada; Hiroyuki Sato; Keiki Takadama
Proc. of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES 2014), 561-575, 2014, Peer-reviwed
International conference proceedings, English - Artificial Bee Colony Algorithm Based on Local Information Sharing in Dynamic Environment
Ryo Takano; Tomohiro Harada; Hiroyuki Sato; Keiki Takadama
PROCEEDINGS OF THE 18TH ASIA PACIFIC SYMPOSIUM ON INTELLIGENT AND EVOLUTIONARY SYSTEMS, VOL 1, SPRINGER INT PUBLISHING AG, 627-641, 2014, Peer-reviwed, This paper focuses on Artificial Bee Colony (ABC) algorithm which can utilize global information in the static environment and extends it to ABC algorithm based on local information sharing (ABC-lis) in dynamic environment. In detail, ABC-lis algorithm shares only local information of solutions unlike the conventional ABC algorithm. To investigates the search ability and adaptability of ABC-lis algorithm to environmental change, we compare it with the conventional two ABC algorithms by applying them to a multimodal problem with dynamic environmental change. The experimental results have revealed that the proposed ABC-lis algorithm can maintain the search performance in the multimodal problem with the dynamic environmental change, meaning that ABC-lis algorithm shows its search ability and adaptability to environmental change.
International conference proceedings, English - 多次元空間問題における商品属性の関係理解と商品選定の支援
沢田石 祐弥; 原田 智広; 佐藤 寛之; 服部 聖彦; 高玉 圭樹; 山口 智浩
電子情報通信学会誌, J97-A, 6, 482-491, 2014, Peer-reviwed
Scientific journal, Japanese - What Is Needed to Promote an Asynchronous Program Evolution in Genetic Programing?
Keiki Takadama; Tomohiro Harada; Hiroyuki Sato; Kiyohiko Hattori
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Verlag, 8426, 227-241, 2014, Peer-reviwed, Unlike a synchronous program evolution in the context of evolutionary computation that evolves individuals (i.e., programs) after evaluations of all individuals in each generation, this paper focuses on an asynchronous program evolution that evolves individuals during evaluations of each individual. To tackle this problem, we explore the mechanism that can promote an asynchronous program evolution by selecting a good individual without waiting for evaluations of all individuals, and investigates its effectiveness in genetic programming (GP) domain. The intensive experiments have revealed the following implications: (1) the program asynchronously evolved with the proposed mechanism can be completed with the shorter execution steps than the program asynchronously evolved without the proposed mechanism
and (2) the program asynchronously evolved with the proposed mechanism can be completed with mostly the same or shorter execution steps than the program synchronously evolved by the conventional GP. © 2014 Springer International Publishing.
International conference proceedings, English - 複数ロボット協調による大規模構造物組み立てにおける故障ロボット回収の影響
大谷 雅之; 佐藤 寛之; 服部 聖彦; 高玉 圭樹
電気学会論文誌. C, 電子・情報・システム部門誌 = The transactions of the Institute of Electrical Engineers of Japan. C, A publication of Electronics, Information and Systems Society, The Institute of Electrical Engineers of Japan, 133, 9, 1729-1737, 2013, Peer-reviwed, This paper focuses on the distributed control of the multiple robots which may be broken and investigates how the robots complete their task by collecting broken robots through the simulation of the large-scale structure assembly. For this purpose, we conduct multiagent simulation for collecting broken robots under the different failure rate of robots. Through the intensive simulations, we have revealed that a collection of broken robots before completing their own task (i.e., deploying their panel) is more effective than after complete their own task.
Scientific journal, Japanese - Variable Space Diversity, Crossover and Mutation in MOEA Solving Many-objective Knapsack Problems
Hiroyuki Sato; Hernán Aguirre; Kiyoshi Tanaka
Annals of Mathematics and Artificial Intelligence, 68, 4, 197-224, 2013, Peer-reviwed, In this work, we analyze variable space diversity of Pareto optimal solutions (POS) and study the effectiveness of crossover and mutation operators in evolutionary many-objective optimization. First we examine the diversity of variables in the true POS on many-objective 0/1 knapsack problems with up to 20 items (bits), showing that variables in POS become noticeably diverse as we increase the number of objectives. We also verify the effectiveness of conventional two-point and uniform crossovers, Local Recombination that selects mating parents based on proximity in objective space, and two-point and uniform crossover operators which Controls the maximum number of Crossed Genes (CCG). We use NSGA-II, SPEA2, IBEAε{lunate} + and MSOPS, which adopt different selection methods, and many-objective 0/1 knapsack problems with n={100,250,500,750,1,000} items (bits) and m = {2,4,6,8,10} objectives to verify the search performance of each crossover operator. Simulation results reveal that Local Recombination and CCG operators significantly improve search performance especially for NSGA-II and MSOPS, which have high diversity of genes in the population. Also, results show that CCG operators achieve higher search performance than Local Recombination for m ≥ 4 objectives and that their effectiveness becomes larger as the number of objectives m increases. In addition, the contribution of CCG and mutation operators for the solutions search is analyzed and discussed. © 2012 Springer Science+Business Media B.V.
Scientific journal, English - 別カテゴリ商品提示による好みの明確化を促す推薦システム
高玉 圭樹; 佐藤 史盟; 大谷 雅之; 服部 聖彦; 佐藤 寛之; 山口 智浩
人工知能学会論文誌, The Japanese Society for Artificial Intelligence, 28, 2, 210-219, 2013, Peer-reviwed, The paper proposes a novel recommender system which supports users to clarify the most appropriate preference by recommending other categories' items that almost meet the attributes selected by users. Such an advantage is achieved by both the preference ncretization of users and the preference change of users.To investigate the effectiveness of the proposed system, we conducted the human-subject experiments and found that the proposed system supports users to find their desirable items by clarifying their preference. Concretely, the following implications have been revealed: (1) the proposed recommender system with both the serendipity and decision buttons enables users to clarify their preference by comparing items which are classified in different categories; (2) in detail, the item recommendation based on the selected item attributes contributes to clarifying the users' preference through a change of their preference, while the item recommendation based on the item characteristic contributes to clarifying the users' preference through a concretization of their preference; and (3) the proposed recommender system with the decision button succeeds the further clarification of the preference of users who have already clarified it.
Scientific journal, Japanese - 二段階の非支配ソーティングと指向性交配による制約付き多目的最適化
宮川 みなみ; 佐藤 寛之
進化計算学会論文誌, 3, 3, 185-196, 2013, Peer-reviwed
Scientific journal, Japanese - Robust Bus Route Optimization to Destruction of Roads
Hiroto Kitagawa; Keiji Sato; Keiki Takadama; Hiroyuki Sato; Kiyohiko Hattori
The Fifth International Workshop on Emergent Intelligence onNetworked Agents (WEIN 2013), at 12th International JointConference on Autonomous Agents and Multi-agent Systems (AAMAS2013), 173-185, 2013, Peer-reviwed
International conference proceedings, English - Towards Understanding of Relationship Among Pareto Optimal Solutions in Multi-dimensional Space via Interactive System
Keiki Takadama; Yuya Sawadaishi; Tomohiro Harada; Yoshihiro Ichikawa; Keiji Sato; Kiyohiko Hattori; Hiroyoki Sato; Tomohiro Yamaguchi
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8018, 3, 137-146, 2013, Peer-reviwed, This paper proposes the interactive system that can help humans to understand the trade-off relationship of Pareto optimal solutions (e.g., good products from a certain aspect) in multi-dimensional space. For this purpose, the following two methods are proposed from the viewpoint of the number of evaluation criteria which should be considered by a user at one time: (i) the two fixed evaluation criteria are employed to evaluate the solutions
and (ii) some evaluation criteria selected by a user (i.e., the number of the evaluation criteria is varied by a user) are employed to evaluate them. To investigate the effectiveness of our proposed system employing either of two methods, we conduct human subject experiments on the motor selection problem and have revealed the following implications: (i) the proposed system based on the two fixed evaluation criteria contributes to helping users to find better motors in terms of all the evaluation criteria, while (ii) the proposed system based on the selected evaluation criteria is more effective to help users to understand Pareto optimal solutions when more evaluation criteria need to be considered. © 2013 Springer-Verlag Berlin Heidelberg.
International conference proceedings, English - A Study on Evolutionary Multi-Objective Racing Car Design Optimization Using a Parallel MOEA/D
Hiroyuki Sato; Minori Imajima; Minami Miyakawa
The 2013 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP2013), 608-611, 2013, Peer-reviwed
International conference proceedings, English - Self-Adaptive Control of the Number of Crossed Genes in Evolutionary Many-Optimization and Its Behavior
Hiroyuki Sato; Carlos A. Coello Coello; Hernán Aguirre; Kiyoshi Tanaka
The 2013 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP2013), 157-160, 2013, Peer-reviwed
International conference proceedings, English - Two-stage Non-dominated Sorting and Directed Mating for Solving Problems with Multi-objectives and Constraints
Minami Miyakawa; Keiki Takadama; Hiroyuki Sato
GECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference, 647-654, 2013, Peer-reviwed, We propose a novel constrained MOEA introducing a parents selection based on a two-stage non-dominated sorting of solutions and directed mating in the objective space. In the parents selection, first, we classify the entire population into several fronts by non-dominated sorting based on constraint violation values. Then, we re-classify each obtained front by non-dominated sorting based on objective function values, and select the parents population from upper fronts. The two-stage non-dominated sorting leads to find feasible solutions having better objective function values in the evolutionary process of infeasible solutions. Also, in the directed mating, we select a primary parent from the parents population and pick solutions dominating the primary parent from the entire population including infeasible solutions. Then we select a secondary parent from the picked solutions and apply genetic operators. The directed mating utilizes valuable genetic information of infeasible solutions to enhance convergence of each primary parent toward its search direction in the objective space. We compare the search performance of the two proposed algorithms using greedy selection (GS) and tournament selection (TS) in the directed mating with the conventional CNSGA-II and RTS algorithms on SRN, TNK, OSY and m objectives k knapsacks problems. We show that the proposed algorithms achieve higher search performance than CNSGA-II and RTS on all benchmark problems used in this work. Copyright © 2013 ACM.
International conference proceedings, English - 交叉する遺伝子数の制御法と解の支配領域の自己制御法による進化型多数目的最適化
佐藤 寛之; エルナン・アギレ; 田中 清
進化計算学会論文誌, 4, 1, 46-56, 2013, Peer-reviwed
Scientific journal, Japanese - 環境変化に適応するためのピボット型一般化
佐藤 圭二; 佐藤 寛之; 高玉 圭樹
計測自動制御学会論文誌, 49, 11, 1020-1028, 2013, Peer-reviwed
Scientific journal, Japanese - 複数ロボット協調による大規模構造物組み立てにおける故障ロボット回収タスクの影響
大谷 雅之; 佐藤寛之; 服部 聖彦; 高玉 圭樹
電気学会論文誌C, 131, 9, 1729-1737, 2013, Peer-reviwed
Scientific journal, Japanese - On the Impact of Path Redundancy Awareness in Evolutionary P2P Networking
Elizabeth Pérez-Cortés; Hiroyuki Sato
Journal of Advanced Computational Intelligence and Intelligent Informatics, Fuji Technology Press, 17, 6, 872-882, 2013, Peer-reviwed, A P2P system is composed by autonomous nodes interconnected to share resources. The interconnections between the nodes define the P2P topology that is traversed to lookup resources. As nodes are autonomous, they are free to decide when to arrive and leave and what resources to share and download. To cope with this dynamism, the Evolutionary P2P Networking approach performs a periodical P2P topology reconfiguration applying evolutionary computation and using the amount of successful lookups as the evaluation function that drives the process. We extended this approach to also consider, as a part of the evaluation function, the creation of redundant paths in the topology and, additionally, we introduced elitism to improve the evolutionary process. In this work we present an extensive evaluation of both approaches. The results show that our approach scales better and produces more connected topologies. The improved connectivity ensures a higher rate of successful lookups under static and dynamic scenarios.
Scientific journal, English - Adaptive Control of the Number of Crossed Genes in Many-objective Evolutionary Optimization
Hiroyuki Sato; Carlos A. Coello Coello; Hernán E. Aguirre; Kiyoshi Tanaka
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7219, 478-484, 2012, Peer-reviwed, To realize effective genetic operation in evolutionary many-objective optimization, crossover controlling the number of crossed genes (CCG) has been proposed. CCG controls the number of crossed genes by using an user-defined parameter α. CCG with small α significantly improves the search performance of multi-objective evolutionary algorithm in many-objective optimization by keeping small the number of crossed genes. However, to achieve high search performance by using CCG, we have to find out an appropriate parameter α by conducting many experiments. To avoid parameter tuning and automatically find out an appropriate α in a single run of the algorithm, in this work we propose an adaptive CCG which adopts the parameter α during the solutions search. Simulation results show that the values of α controlled by the proposed method converges to an appropriate value even when the adaptation is started from any initial values. Also we show the adaptive CCG achieves more than 80% with a single run of the algorithm for the maximum search performance of the static CCG using an optimal α. © 2012 Springer-Verlag.
International conference proceedings, English - Roles of CCG Crossover and Mutation in Evolutionary Many-objective Optimization
Hiroyuki Sato; Hernán Aguirre; Kiyoshi Tanaka
2012 WORLD AUTOMATION CONGRESS (WAC), IEEE, 1-6, 2012, Peer-reviwed, When we solve many-objective optimization problems (MaOPs) by using multi-objective evolutionary algorithms (MOEAs), genetic diversity of solutions in the population significantly increases in order to explore the true Pareto optimal solutions widely distributed in variable space. In MOEAs, if solutions in the population become noticeably diverse in variable space, conventional crossovers become too disruptive genetic operator and decrease its effectiveness. To overcome this problem in MaOPs, crossover controlling the number of crossed genes (CCG) has been proposed. CCG controls the number of crossed genes by using an user-defined parameter alpha. CCG with small alpha significantly improves the search performance of MOEAs in MaOPs by keeping small the number of crossed genes. CCG operator has a similar feature to mutation operator in the sense that both operators can control the amount of genetic variation to create offspring. To clarify the roles of CCG crossover and mutation in MaOPs, in this work we analyze the search performance of CCG-only, mutation-only and combined CCG-mutation based search on many-objective 0/1 knapsack problems. Simulation results reveal that mutation induces diversity in the Pareto front and CCG crossover enhances convergence towards the true Pareto front. Also, we show that highest search performance, realizing well-balanced search between convergence and diversity, can be achieved by combined CCG-mutation rather than by mutation-only or CCG-only based search.
International conference proceedings, English - 進化型多数目的最適化における交叉遺伝子数の自己適応：多数目的0/1 ナップザック問題における性能検証
佐藤 寛之; カルロス・コエロ; エルナン・アギレ; 田中 清
進化計算学会論文誌, 3, 3, 122-132, 2012, Peer-reviwed
Scientific journal, Japanese - Evolutionary Optimization for Feeder Route Network Using Multi-Objective Clustering
Keiji Sato; Saori Iseya; Keiki Takadama; Hiroyuki Sato; Kiyohiko Hattori
International Journal of Advancements in Computing Technology, 4, 22, 269-280, 2012, Peer-reviwed, This paper extends the demand priority-based Genetic Algorithm (namely the demand pri-GA) by employing the cluster-first-route-second method to generate the effective feeder route network consisted of the hub-spoke and loop type networks. For this purpose, we propose the feeder route network optimization method which integrates the demand pri-GA with the multi-objective clustering in order not to fall into local minima of the feeder route network. Through intensive simulations on the Busan-centered feeder route network optimization problem, we have revealed that (1) the proposed method contributes to avoiding to fall into local minima such as the feeder route network which including the high cost routes
and (2) a relaxation of cluster restrictions in the proposed method contributes to generating a various low cost feeder routes network.
Scientific journal, English - Sleep Stage Estimation by Evolutionary Computation Using Heartbeat Data and Body-Movement
Hiroyasu Matsushima; Kazuyuki Hirose; Kiyohiko Hattori; Hiroyuki Sato; Keiki Takadama
International Journal of Advancements in Computing Technology, 4, 22, 281-290, 2012, Peer-reviwed, This paper focuses on distinctive changes of not only the heart rate but also the body movement in REM stage (i.e., light sleep) and Non-REM stage (i.e., deep sleep) and improves our sleep estimation method by employing the feature of such distinctive changes. In particular, the heart rate increases irregularly in REM stage, while the heart rate decreases in Non-REM stage. The body moves intensively in REM stage, while the body does not frequently move in Non-REM stage. Using such distinctive changes, we propose a new fitness function which determines the REM/Non-REM stage and introduce it into for our sleep estimation method based on Genetic Algorithms (GAs), which evolve the sleep stage for each person according to the fitness. To investigate an effectiveness of a new fitness function, we compare the estimated sleep stages of our method employing the proposed fitness function with that of Watanabe's method as the conventional method. The experimental results suggest that our method employing the proposed fitness function has a capability to estimate the sleep stage accurately than Watanabe's method without connecting any devices.
Scientific journal, English - Effects of Two-Stage Non-Dominated Sorting and Directed Mating in Constrained MOEAs
Minami Miyakawa; Hiroyuki Sato
Proc. of 2012 International Workshop on Modern Science and Technology (IWMST 2012), 372-377, 2012, Peer-reviwed
International conference proceedings, English - Local Recombination Controlling Bit Swap Probability in Evolutionary Many-Objective Optimization
Masaharu Horino; Hiroyuki Sato
Proc. of 2012 International Workshop on Modern Science and Technology (IWMST 2012), 394-399, 2012, Peer-reviwed
International conference proceedings, English - Dynamic Control of the Number of Crossed Genes in Evolutionary Many-Objective Optimization
Hiroyuki Sato; Carlos A. Coello Coello; Hernán Aguirre; Kiyoshi Tanaka
6TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING AND INTELLIGENT SYSTEMS, AND THE 13TH INTERNATIONAL SYMPOSIUM ON ADVANCED INTELLIGENT SYSTEMS, IEEE, 1435-1440, 2012, Peer-reviwed, When multi-objective evolutionary algorithms (MOEAs) are applied to many-objective optimization problems (MaOPs), genetic diversity of solutions in the population significantly increases to explore the true Pareto optimal solutions distributed in broad region of variable space. In MOEAs, if genetic diversity of solutions in the population become noticeably diverse, conventional crossovers become too disruptive and decrease its effectiveness. To realize effective genetic operation in MaOPs, crossover controlling the number of crossed genes (CCG) has been proposed. CCG controls the number of crossed genes by using an user-defined parameter alpha. CCG using a small alpha remarkably improves the search performance of MOEA especially in MaOPs by restricting the number of crossed genes. The conventional CCG uses a fixed value of alpha throughout a single run of MOEA, so that the number of crossed genes does not change during the solutions search. However, since the population dynamically changes during the evolution, the optimal number of crossed genes will change during the solutions search. To further improve the search performance of MOEAs in MaOPs, in this work we propose a dynamic CCG which dynamically controls alpha according to the number of generations. Simulation results focusing on many-objective 0/1 knapsack problems show that dynamic CCG reducing alpha during the solutions search achieves higher search performance than the conventional CCG using an optimal fixed alpha. Also, we show that convergence and diversity property of the obtained solutions are emphasized by dynamic control of alpha.
International conference proceedings, English - An Evolutionary Algorithm Using Two-stage Non-dominated Sorting and Directed Mating for Constrained Multi-objective Optimization
Minami Miyakawa; Hiroyuki Sato
6TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING AND INTELLIGENT SYSTEMS, AND THE 13TH INTERNATIONAL SYMPOSIUM ON ADVANCED INTELLIGENT SYSTEMS, IEEE, 1441-1446, 2012, Peer-reviwed, When multi-objective optimization problems (MOPs) include several constraints, MOEAs need to introduce a mechanism to obtain feasible solutions from infeasible ones. CNSGA-II, a representative constrained MOEA, evolves infeasible solutions into feasible ones by using the concept of constrain-dominance based on the sum of constraint violation values. However, since the conventional CNSGA-II considers only the sum of constraint violation values in the evolution process of infeasible solutions, objective function values of obtained feasible solutions would be worse. Also, since infeasible solutions have less chance to generate offspring than feasible ones, valuable genetic information of infeasible solutions would not be utilized in the solutions search. To overcome these problems and improve the search performance of MOEAs on constrained MOPs, in this work we propose a novel constrained MOEA introducing a parents selection based on two-stage non-dominated sorting of solutions and a directed mating in objective space. We compare the search performance of the proposed algorithm with CNSGA-II on BNH, SRN, TNK, OSY and m objectives k knapsacks problems, and we show that the proposed algorithm achieves higher search performance than CNSGA-II on all benchmark problems.
International conference proceedings, English - A Study on Adaptive Control of Dominance Area in Evolutionary Many-Objective Optimization
Kohei Tomita; Minami Miyakawa; Hiroyuki Sato
The 16th Asia Pacific Symposium of Intelligent and Evolutionary Systems (IES2012), 8-14, 2012, Peer-reviwed
International conference proceedings, English - Evolutionary P2P Networking Enhancing Link Diversity
Elizabeth Perez-Cortes; Hiroyuki Sato
The 16th Asia Pacific Symposium of Intelligent and Evolutionary Systems (IES2012), 19-24, 2012, Peer-reviwed
International conference proceedings, English - Ship Route Optimization by Multi-Objective EvolutionaryComputation Based on Dynamic Reference Point
Eriko Azuma; Keiji Sato; Hiroyuki Sato; Kiyohiko Hattori; Keiki Takadama
International Conference on Humanized System 2012 (ICHS 2012), 209-214, 2012, Peer-reviwed
International conference proceedings, English - Cluster-based Bus Route Optimization for Stranded Persons in Disaster
Hiroto Kitagawa; Keiji Sato; Hiroyuki Sato; Kiyohiko Hattori; Keiki Takadama
International Conference on Humanized System 2012 (ICHS 2012), 224-227, 2012, Peer-reviwed
International conference proceedings, English - Layout Optimization of Advertisements on News Websites by Genetic Algorithm
Noriyuki Muramatsu; Keiki Takadama; Hiroyuki Sato; Maki Sakamoto
International Workshop on Modern Science and Technology (IWMST 2012), 384-389, 2012, Peer-reviwed
International conference proceedings, English - Optimizing Ship Routes by Evolutionary Computation in CompetitiveShip Companies
Eriko Azuma; Keiji Sato; Hiroyuki Sato; Kiyohiko Hattori; Keiki Takadama
International Workshop on Modern Science and Technology (IWMST 2012), 378-383, 2012, Peer-reviwed
International conference proceedings, English - Robust Bus Route Optimization by Connecting/Extending Routes
Hiroto Kitagawa; Keiji Sato; Hiroyuki Sato; Kiyohiko Hattori; Keiki Takadama
International Workshop on Modern Science and Technology (IWMST 2012), 390-393, 2012, Peer-reviwed
International conference proceedings, English - Towards Network Optimization of Regular and Non-regular Flights
Takahiro Jinba; Hiroto Kitagawa; Eriko Azuma; Keiji Sato; Hiroyuki Sato; Kiyohiko Hattori; Keiki Takadama
The 16th International Symposium on Intelligent and EvolutionarySystems (IES 2012), 124-128, 2012, Peer-reviwed
International conference proceedings, English - Preference Clarification Recommender System by Searching ItemsBeyond Category
Keiki Takadama; Fumiaki Sato; Masayuki Otani; Kiyohiko Hattori; Hiroyuki Sato; Tomohiro Yamaguchi
The IADIS Interfaces and Human Computer Interaction 2012Conference (IHCI 2012), 3-10, 2012, Peer-reviwed
International conference proceedings, English - Genetic Diversity and Effective Crossover in Evolutionary Many-objective Optimization
Hiroyuki Sato; Hernán Aguirre; Kiyoshi Tanaka
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6683, 91-105, 2011, Peer-reviwed, In this work, we analyze genetic diversity of Pareto optimal solutions (POS) and study effective crossover operators in evolutionary many-objective optimization. First we examine the diversity of genes in the true POS on many-objective 0/1 knapsack problems with up to 20 items (bits), showing that genes in POS become noticeably diverse as we increase the number of objectives. We also verify the effectiveness of conventional two-point crossover, Local Recombination that selects mating parents based on proximity in objective space, and two-point and uniform crossover operators Controlling the maximum number of Crossed Genes (CCG). We use NSGA-II, SPEA2, IBEA e+ and MSOPS, which adopt different selection methods, and many-objective 0/1 knapsack problems with n = {100,250,500,750,1000} items (bits) and m = {2,4,6,8,10} objectives to verify the search performance of each crossover operator. Simulation results reveal that Local Recombination and CCG operators significantly improve search performance especially for NSGA-II and MSOPS, which have high diversity of genes in the population. Also, results show that CCG operators achieve higher search performance than Local Recombination for m ≥ 4 objectives and that their effectiveness becomes larger as the number of objectives m increases. © Springer-Verlag Berlin Heidelberg 2011.
International conference proceedings, English - Improved S-CDASusing Crossover Controlling the Number of Crossed Genes for Many-objective Optimizatio
Hiroyuki Sato; Hernán Aguirre; Kiyoshi Tanaka
GECCO-2011: PROCEEDINGS OF THE 13TH ANNUAL GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, ASSOC COMPUTING MACHINERY, 753-760, 2011, Peer-reviwed, Self-controlling dominance area of solutions (S-CDAS) reclassifies solutions in each front obtained by non-domination sorting to realize fine-grained ranking of solutions and improve the search performance of multi-objective evolutionary algorithms (MOEAs) in many-objective optimization problems (MaOPs). In this work, we further improve search performance of S-CDAS in MaOPs by analyzing genetic diversity in many-objective problems and enhancing crossover operators. First, we analyze genetic diversity in the population and the contribution of the conventional genetic operators when we increase the number of objectives, showing that the genetic diversity in the population significantly increases and offspring created by conventional crossover come to be not selected as parents because the operator becomes too disruptive and its effectiveness decrease. To overcome this problem, we implement crossover controlling the number of crossed genes (CCG) in S-CDAS and verify its effectiveness. Through performance verification using many-objective knapsack problems with 4-10 objectives, we show that the search performance of S-CDAS noticeably improves when we restrict the number of crossed genes. Also, we show that the effectiveness of CCG operator becomes significant as we increase the number of objectives. Furthermore, we show that offspring created by CCG are selected as parents more often than conventional crossover.
International conference proceedings, English - Robustness to Bit Inversion in Registers and Acceleration of Program Evolution in On-Board Computer
Tomohiro Harada; Masayuki Otani; Yoshihiro Ichikawa; Kiyohiko Hattori; Hiroyuki Sato; Keiki Takadama
Journal of Advanced Computational Intelligence and Intelligent Informatics, Fuji Technology Press, 15, 8, 1175-1185, 2011, Peer-reviwed, This paper focuses on an on-board computer (OBC) that evolves computer programs through bit inversion and targets analyzing robustness against bit inversion in registers. We also propose a new method that can change the number of computer programs dynamically. Intensive experiments revealed the following: (1) Correct programs can be maintained even in bit inversion in registers in addition to bit inversion in instructions. (2) Our proposal accelerates program evolution by increasing the population size, i.e., the number of programs, within fixed memory size.
Scientific journal, English - Improving Recovery Capability of Multiple Robots in Different Scale Structure Assembly
Masayuki Otani; Kiyohiko Hattori; Hiroyuki Sato; Keiki Takadama
Journal of Advanced Computational Intelligence and Intelligent Informatics, Fuji Technology Press, 15, 8, 1186-1196, 2011, Peer-reviwed, This paper focuses on the distributed control of multiple robots that may be broken and investigates recovery capability, which means how robots can complete assembly, when some are broken, through the assembly of a different-scale solar-powered satellite. We thus conduct simulation at different failure rates of robots that use our proposed deadlock avoidance. Through intensive simulation, we show that (1) our proposed method with no information sharing keeps high recovery capability and (2) this method is robust against differences in structure scale.
Scientific journal, English - What Kinds of Human Negotiation Skill Can Be Acquired by Changing Negotiation Order of Bargaining Agents?
Keiki Takadama; Atsushi Otaki; Keiji Sato; Hiroyasu Matsushima; Masayuki Otani; Yoshihiro Ichikawa; Kiyohiko Hattori; Hiroyoki Sato
HUMAN INTERFACE AND THE MANAGEMENT OF INFORMATION: INTERACTING WITH INFORMATION, PT 2, SPRINGER-VERLAG BERLIN, 6772, 335-344, 2011, Peer-reviwed, This paper focuses on developing human negotiation skills through interactions between a human player and a computer agent, and explores its strategic method towards a human skill improvement in enterprise. For this purpose, we investigate the negotiation skill development through bargaining game played by the player and an agent. Since the acquired negotiation strategy of the players is affected by the negotiation order of the different types of agents, this paper aims at investigating what kind of the negotiation strategies can be learned by negotiating with different kinds of agents in order. Through an intensive human subject experiment, the following implications have been revealed: (1) human players, negotiating with the human-like behavior agent firstly and the strong/weak attitude agent secondly, can neither obtain the large payoff nor win many games, while (2) human players, negotiating with the strong/weak attitude agent firstly and the human-like behavior agent secondly, can obtain the large payoff and win many games.
International conference proceedings, English - The Biased Multi-objective Optimization Using the Reference Point: Toward the Industrial Logistics Network
Eriko Azuma; Tomohiro Shimada; Keiki Takadama; Hiroyuki Sato; Kiyohiko Hattori
Proceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011, 2, 27-30, 2011, Peer-reviwed, This paper explores the multi-objective evolutionary algorithm that can effectively solve a multi-objective problem where an importance of the objective differs each other unlike the conventional problem which concerns each objective evenly. Since such a type of a problem is often found in industrial problems (e.g., logistics network), we propose the biased multi-objective optimization using the reference point (i.e., the factor of strongly concerned). Intensive experiment on the multi-objective knapsack problem had revealed that our proposed method was more superior and had higher diversity than the conventional multi-objective optimization method. © 2011 IEEE.
International conference proceedings, English - Sleep Stage Estimation By Evolutionary Computation Using Heartbeat Data and Body-Movement
Hiroyasu Matsushima; Kazuyuki Hirose; Kiyohiko Hattori; Hiroyuki Sato; Keiki Takadama
The 15th International Symposium on Intelligent and Evolutionary Systems (IES 2011), 103-110, 2011, Peer-reviwed
International conference proceedings, English - Evolutionary Optimization for Feeder Route Network Using Multi-Objective Clustering
Keiji Sato; Saori Iseya; Keiki Takadama; Hiroyuki Sato; Kiyohiko Hattori
The 15th International Symposium on Intelligent and Evolutionary Systems (IES 2011), 76-83, 2011, Peer-reviwed
International conference proceedings, English - Cooperation Among Multiple Robots by Collecting Broken Robots in Large-Scale Structure Assembly
Masayuki Otani; Kiyohiko Hattori; Hiroyuki Sato; Keiki Takadama
The 2011 IEEE/SICE International Symposium on System Integration (SII 2011), 691-696, 2011, Peer-reviwed
International conference proceedings, English - Large-scale Structure Assembly by Multiple Robots Which May Be Broken
Masayuki Otani; Kiyohiko Hattori; Hiroyuki Sato; Keiki Takadama
Lecture Notes in Electrical Engineering, 67, 567-576, 2010, Peer-reviwed, This paper investigates how to design the limit of failure rate and the adjust number of robots in the distribution control of the multiple robots which may be broken through the simulation of space solar power satellite assembly. For this purpose, we conduct simulations with changing the failure rate of robots that employ our proposed deadlock avoidance method. Intensive simulations have revealed the following implications: (1) from the viewpoint of the completion rate, our deadlock avoidance method enables the robots to complete the assembly in 80% completion rate even if the 1/3 robots are broken
(2) from the viewpoint of the recovery rate (i.e., the rate of completing a task when some of robots are broken), the maximum failure rate which enables robots to complete the assembly in 80% is 0.2%, i.e., the 1/3 robots can be broken
and (3) when the failure rate is 0.2%, it is possible to maximize the completion rate from 80% to 90% by adjust the number of the robots. © 2010 Springer-Verlag Berlin Heidelberg.
International conference proceedings, English - Pareto Partial Dominance MOEA and Hybrid Archiving Strategy Included CDAS in Many-Objective Optimization
Hiroyuki Sato; Hernán Aguirre; Kiyoshi Tanaka
2010 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), IEEE, 3720-3727, 2010, Peer-reviwed, In this work, we propose a novel multi-objective evolutionary algorithm (MOEA) that uses Pareto partial dominance, which calculates dominance between solutions using only r objective functions selected from m objective functions to induce appropriate selection pressure in the evolution process of MOEA. Also, we temporally switch r objective functions among C-m(r) combinations in every interval generations I-g to optimize all of the objective functions throughout the entire evolution process. In this work, we use many-objective 0/1 knapsack problems to verify the search performance of the proposed Pareto partial dominance MOEA (PPD-MOEA). Simulation results show that there is an optimum value for the number of objective functions r to be considered in Pareto partial dominance, and the interval (generation numbers) I-g to maximize the entire search performance. Also, the search performance of PPD-MOEA is superior to NSGA-II and recent state-of-the-art MOEAs, i.e., IBEA, CDAS and MSOPS. Additionally, to further enhance the search performance of PPD-MOEA, we propose a hybrid archiving strategy which uses both conventional NSGA-II and CDAS to select well-spread and well-converged solutions simultaneously when updating the archive population. Simulation results show that the hybrid archiving strategy further improves the search performance of PPD-MOEA by enhancing convergence while maintaining diversity in the archive population.
International conference proceedings, English - A Study on Archiving Strategy in Partial Dominance MOEA
Hiroyuki Sato; Hernán Aguirre; Kiyoshi Tanaka
Proc. 2010 International Symposium on Intelligent Systems (iFAN 2010), in USB memory, 1-6, 2010, Peer-reviwed
International conference proceedings, English - A Study on Interval to Switch Combination of Objectives Considered in Pareto Partial Dominance MOEA
Hiroyuki Sato; Hernán Aguirre; Kiyoshi Tanaka
Proc. 2010 World Automation Congress (WAC 2010), in CD-ROM, 1-6, 2010, Peer-reviwed
International conference proceedings, English - Self-Controlling Dominance Area of Solutions in Evolutionary Many-Objective Optimization
Hiroyuki Sato; Hernán Aguirre; Kiyoshi Tanaka
SIMULATED EVOLUTION AND LEARNING, SPRINGER-VERLAG BERLIN, 6457, 455-465, 2010, Peer-reviwed, Controlling dominance area of solutions (CDAS) relaxes the concepts of Pareto dominance with an user-defined parameter S. This method enhances the search performance of dominance-based MOEA in many-objective optimization problems (MaOPs). However, to bring out desirable search performance, we have to experimentally find out S that controls dominance area appropriately. Also, there is a tendency to deteriorate the diversity of solutions obtained by CDAS when we decrease S from 0.5. To solve these problems, in this work, we propose a modification of CDAS called self-controlling dominance area of solutions (S-CDAS). In S-CDAS, the algorithm self-controls dominance area for each solution without the need of an external parameter. S-CDAS considers convergence and diversity and realizes a fine grained ranking that is different from conventional CDAS. In this work, we use many-objective 0/1 knapsack problems with m = 4 similar to 10 objectives to verify the search performance of the proposed method. Simulation results show that S-CDAS achieves well-balanced search performance on both convergence and diversity compared to conventional NSCA-II, CDAS, IBEA(epsilon+) and MSOPS.
International conference proceedings, English - 多数目的0/1ナップザック問題における部分支配を時間的に切り替えるMOEAの効果
佐藤 寛之; エルナン・アギレ; 田中 清
人工知能学会論文誌, The Japanese Society for Artificial Intelligence, 25, 2, 320-331, 2010, Peer-reviwed, In this work, we propose a novel multi-objective evolutionary algorithm (MOEA) which improves search performance of MOEA especially for many-objective combinatorial optimization problems. Pareto dominance based MOEAs such as NSGA-II and SPEA2 meet difficulty to rank solutions in the population noticeably deteriorating search performance as we increase the number of objectives. In the proposed method, we rank solutions by calculating Pareto partial dominance between solutions using r objective functions selected from m objective functions to induce appropriate selection pressure in many-objective optimization by Pareto-based MOEA. Also, we temporally switch r objective functions among mCr combinations in every interval generations Ig to optimize all of the objective functions throughout the entire evolution process. In this work, we use many-objective 0/1 knapsack problems to show the search performance of the proposed method and analyze its evolution behavior. Simulation results show that there is an optimum value for the number of objective functions r to be considered for the calculation of Pareto partial dominance and the interval (generation numbers) Ig to maximize the entire search performance. Also, the search performance of the proposed method is superior to recent state-of-the-art MOEAs, i.e., IBEA, CDAS and MSOPS. Furthermore, we show that the computational time of the proposed method is much less than IBEA, CDAS and MSOPS, and comparative or sometimes less than NSGA-II.
Scientific journal, Japanese - 解の支配領域の自己制御による進化型多数目的最適化: 多数目的 0/1 ナップザック問題における性能検証と挙動解析
佐藤 寛之; エルナン・アギレ; 田中 清
進化計算学会論文誌, The Japanese Society for Evolutionary Computation, 1, 1, 32-42, 2010, Peer-reviwed, Controlling dominance area of solutions (CDAS) relaxes the concepts of Pareto dominance with an user defined parameter S. This method enhances the search performance of dominance-based MOEA in many-objective optimization problems (MaOPs). However, to bring out desirable search performance, we have to experimentally find out S that controls dominance areas appropriately. Also, there is a tendency to deteriorate the diversity of solutions obtained by CDAS when we decrease S from 0.5. To solve these problems, in this work, we propose a modification of CDAS called self-controlling dominance area of solutions (S-CDAS). In S-CDAS, the algorithm self-controls dominance areas for each solution without the need of an external parameter. S-CDAS considers convergence and diversity and realizes a fine grained ranking that is different from conventional CDAS. In this work, we focus on combinatorial optimization and use many-objective 0/1 knapsack problems with m = 4∼10 objectives to verify the search performance of the proposed method. Simulation results show that S-CDAS achieves well-balanced search performance on both convergence and diversity compared to conventional NSGA-II, CDAS, IBEAε+ and MSOPS. In addition, the algorithm's behavior of S-CDAS is analyzed and discussed.
Scientific journal, Japanese - Improving Sleep Stage Estimation by Specializing Multiple Band-Pass Filters and Discrete Heartbeat Data
Keiki Takadama; Kazuyuki Hirose; Hiroyasu Matsushima; Kiyohiko Hattori; Hiroyuki Sato; Nobuo Nakajima
The Fourth International Symposium on Medical Information and Communication Technology (ISMICT 2010), 0-0, 2010, Peer-reviwed
International conference proceedings, English - Hybrid Directional-Biased Evolutionary Algorithm for Multi-Objective Optimization
Tomohiro Shimada; Masayuki Otani; Hiroyasu Matsushima; Hiroyuki Sato; Kiyohiko Hattori; Keiki Takadama
PARALLEL PROBLEM SOLVING FROM NATURE-PPSN XI, PT II, SPRINGER-VERLAG BERLIN, 6239, 121-130, 2010, Peer-reviwed, This paper proposes the hybrid Indicator-based Directional-biased Evolutionary Algorithm (hIDEA) and verifies its effectiveness through the simulations of the multi-objective 0/1 knapsack problem. Although the conventional Multi-objective Optimization Evolutionary Algorithms (MOEA5) regard the weights of all objective functions as equally, hIDEA biases the weights of the objective functions in order to search not only the center of true Pareto optimal solutions but also near the edges of them. Intensive simulations have revealed that hIDEA is able to search the Pareto optimal solutions widely and accurately including the edge of true ones in comparison with the conventional methods.
International conference proceedings, English - Dynamic Matching Range in Exemplar-Based Learning Classifier System
Hiroyasu Matsushima; Kiyohiko Hattori; Hiroyuki Sato; Keiki Takadama
2010 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), IEEE, 1975-1982, 2010, Peer-reviwed, This paper proposes the extended version of Exemplar-based Learning Classifier System (ECS) called DMR-ECS which introduces the basis function for the dynamic matching selection in ECS. In comparision with our previous match selection in ECS, the proposed dynamic match selection in DMR-ECS can control an appropriate range of the match selection automatically to extract the exemplars that cover given problem space. Intensive simulation on the cargo layout problem has revealed that DMR-ECS contributes to not only improving the performance but also reducing the number of the exemplars with an appropriate range of the match selection.
International conference proceedings, English - Robustness to Bit Inversion in Registers and Acceleration of Program Evolution in On-Board Computer
Tomohiro Harada; Masayuki Otani; Yoshihiro Ichikawa; Kiyohiko Hattori; Hiroyuki Sato; Keiki Takadama
The 10th International Symposium on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS2010), 802-809, 2010, Peer-reviwed
International conference proceedings, English - Improving Recovery Capability of Multiple Robots in Different Scale Structure Assembly
Masayuki Otani; Kiyohiko Hattori; Hiroyuki Sato; Keiki Takadama
The 10th International Symposium on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS 2010), 873-880, 2010, Peer-reviwed
International conference proceedings, English - Towards Care Plans of Aged Persons by Multi-Objective Optimization
Tomohiro Shimada; Hiroyasu Matsushima; Hiroyuki Sato; Kiyohiko Hattori; Keiki Takadama
SICE Annual Conference 2010, SB15-5, 3258-3263, 2010, Peer-reviwed
International conference proceedings, English - Towards an Objective Generation As an Autonomous Agent Architecture
Ayano Kanamaru; Kiyohiko Hattori; Hiroyuki Sato; Keiki Takadama
SICE Annual Conference 2010, SA15-1, 2763-2768, 2010, Peer-reviwed
International conference proceedings, English - Serendipity-Based Recommender System in Web Shopping
Fumiaki Sato; Atushi Otaki; Kiyohiko Hattori; Hiroyuki Sato; Keiki Takadama
The IADIS Interfaces and Human Computer Interaction 2010 Conference (IHCI 2010), 440-442, 2010, Peer-reviwed
International conference proceedings, English - Robustness of Deadlock Avoidance in Assembling Large-scale Structure by Multiple Robots Having Trouble and Individual Differences
Masayuki Otani; Kiyohiko Hattori; Hiroyuki Sato; Keiki Takadama
The 18th IFAC Symposium on Automatic Control in Aerospace (ACA 2010), 0-0, 2010, Peer-reviwed
International conference proceedings, English - Local Dominance MOEA Including Control of Dominance Area of Solutions on 0/1 Multiobjective Knapsack Problems
Hiroyuki Sato; Hernán Aguirre; Kiyoshi Tanaka
Transactions of the Japanese Society for Artificial Intelligence, Japanese Society for Artificial Intelligence, 24, 1, 69-79, 2009, Peer-reviwed, Local dominance has been shown to improve significantly the overall performance of multiobjective evolutionary algorithms (MOEAs) on combinatorial optimization problems. This work proposes the control of dominance area of solutions in local dominance MOEAs to enhance Pareto selection aiming to find solutions with high convergence and diversity properties. We control the expansion or contraction of the dominance area of solutions and analyze its effects on the search performance of a local dominance MOEA using 0/1 multiobjective knapsack problems. We show that convergence can be significantly improved while keeping a good distribution of solutions along the whole true Pareto front by using the local dominance MOEA with expansion of dominance area of solutions. We also show that dominance can be applied within very small neighborhoods by controlling the dominance area of solutions, which reduces significantly the computational cost of the local dominance MORA.
Scientific journal, Japanese - Pareto Partial Dominance MOEA in Many-Objective Optimization
Hiroyuki Sato; Hernán Aguirre; Kiyoshi Tanaka
Proc. of the 2009 AI*IA Workshop on Complexity, Evolution and Emergent Intelligence, in CD-ROM, 1-10, 2009, Peer-reviwed
International conference proceedings, English - Analysis of NSGA-II and NSGA-II with CDAS, and Proposal of an Enhanced CDAS Mechanism
Kyoko Tsuchida; Hiroyuki Sato; Hernán Aguirre; Kiyoshi Tanaka
Journal of Advanced Computational Intelligence and Intelligent Informatics, Fuji Technology Press, 13, 4, 470-480, 2009, Peer-reviwed, In this work, we analyze the functionality transition in the evolution process of NSGA-II and an enhanced NSGA-II with the method of controlling dominance area of solutions (CDAS) from the viewpoint of front distribution. We examine the relationship between the population of the first front consisting of nondominated solutions and the values of two metrics, NORM and ANGLE, which measure convergence and diversity of Pareto-optimal solutions (POS), respectively. We also suggest potentials to further improve the search performance of the enhanced NSGA-II with CDAS by emphasizing the parameter S, which controls the degree of dominance by contracting or expanding the dominance area of solutions, before and after the boundary generation of functionality transition. Furthermore, we analyze the behavior of the evolution of the enhanced NSGA-II with CDAS using the best parameters combination and compare its performance with two other algorithms that enhance selection of NSGA-II.
Scientific journal, English - Local Dominance and Controlling Dominance Area of Solutions in Multi and Many Objectives EAs
Hiroyuki Sato; Hernán Aguirre; Kiyoshi Tanaka
Lead, Graduate Student Workshop in 2008 Genetic and Evolutionary Computation Conference (GECCO 2008), in CD-ROM, 1811-1814, 2008, Peer-reviwed
International conference proceedings, English - Functionality Transition in the Evolution Process of NSGA-II and Potentials for Performance Improvement
Kyoko Tsuchida; Hiroyuki Sato; Hernán Aguirre; Kiyoshi Tanaka
Proc. Joint 4th International Conference on Soft Computing and Intelligent Systems and 9th International Symposium on advanced Intelligent Systems (SCIS&ISIS 2008), 1043-1048, 2008, Peer-reviwed
International conference proceedings, English - Enhancing Multiobjective Evolutionary Algorithms by Local Dominance and Local Recombination: Performance Verification in Multiobjective 0/1 Knapsack Problems
Hiroyuki Sato; Hernán Aguirre; Kiyoshi Tanaka
IPSJ Trans. Mathematical Modeling and Its Applications, Information and Media Technologies Editorial Board, Vol.48, No.SIG12 (TOM16), 98-113, 2007, Peer-reviwed, This paper proposes a method to enhance single population multiobjective evolutionary algorithms (MOEAs) by searching based on local dominance and local recombination. In this method, first, all fitness vectors of individuals are transformed to polar coordinate vectors in objective function space. Then, the population is iteratively divided into several subpopulations by using declination angles. As a result, each sub-population covers a sub-region in the multiobjective space with its individuals located around the same search direction. Next, local dominance is calculated separately for each sub-population after alignment of its principle search direction by rotation. Selection, recombination, and mutation are applied to individuals within each sub-population. The proposed method can improve the performance of MOEAs that use dominance based selection, and can reduce the entire computational cost to calculate dominance among solutions as well. In this paper we verify the effectiveness of the proposed method obtaining Pareto optimal solutions in two representative MOEAs, i.e. NSGA-II and SPEA2, with Multiobjective 0/1 Knapsack Problems.
Scientific journal, English - Controlling Dominance Area of Solutions and Its Impact on the Performance of MOEAs
Hiroyuki Sato; Hernán Aguirre; Kiyoshi Tanaka
EVOLUTIONARY MULTI-CRITERION OPTIMIZATION, PROCEEDINGS, SPRINGER-VERLAG BERLIN, 4403, 5-20, 2007, Peer-reviwed, This work proposes a method to control the dominance area of solutions in order to induce appropriate ranking of solutions for the problem at hand, enhance selection, and improve the performance of MOEAs on combinatorial optimization problems. The proposed method can control the degree of expansion or contraction of the dominance area of solutions using a user-defined parameter S. Modifying the dominance area of solutions changes their dominance relation inducing a ranking of solutions that is different to conventional dominance. In this work we use 0/1 multiobjective knapsack problems to analyze the effects on solutions ranking caused by contracting and expanding the dominance area of solutions and its impact on the search performance of a multiobjective optimizer when the number of objectives, the size of the search space, and the complexity of the problems vary. We show that either convergence or diversity can be emphasized by contracting or expanding the dominance area. Also, we show that the optimal value of the area of dominance depends strongly on all factors analyzed here: number of objectives, size of the search space, and complexity of the problems.
International conference proceedings, English - Local Dominance Including Control of Dominance Area of Solutions in MOEAs
Hiroyuki Sato; Hernán Aguirre; Kiyoshi Tanaka
2007 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE IN MULTI-CRITERIA DECISION MAKING, IEEE, 310-317, 2007, Peer-reviwed, Local dominance has been shown to improve significantly the overall performance of multiobjective evolutionary algorithms (MOEAs) on combinatorial optimization problems. This work proposes the control of dominance area of solutions in local dominance MOEAs to enhance Pareto selection aiming to find solutions with high convergence and diversity properties. We control the expansion or contraction of the dominance area of solutions and analyze its effects on the search performance of a local dominance MOEA using 0/1 multiobjective knapsack problems. We show that convergence of the algorithm can be significantly improved while keeping a good distribution of solutions along the whole true Pareto front by using local dominance with expansion of dominance area of solutions. We also show that by controlling the dominance area of solutions dominance can be applied within very small neighborhoods, which reduces significantly the computational cost of the local dominance MOEA.
International conference proceedings, English - Controlling Dominance Area of Solutions in Multiobjective Evolutionary Algorithms and Performance Analysis on Multiobjective 0/1 Knapsack Problems
Hiroyuki Sato; Hernán Aguirre; Kiyoshi Tanaka
IPSJ Trans. Mathematical Modeling and Its Applications, Information and Media Technologies Editorial Board, Vol.48, No.SIG15 (TOM18), 137-152, 2007, Peer-reviwed, This work proposes a method to control the dominance area of solutions in order to induce appropriate ranking of solutions for the problem at hand, enhance selection, and improve the performance of MOEAs on combinatorial optimization problems. The proposed method can control the degree of expansion or contraction of the dominance area of solutions using a user-defined parameter S. Modifying the dominance area of solutions changes their dominance relation inducing a ranking of solutions that is different to conventional dominance. In this work we use 0/1 multiobjective knapsack problems to analyze the effects on solutions ranking caused by contracting and expanding the dominance area of solutions and its impact on the search performance of a multi-objective optimizer when the number of objectives, the size of the search space, and the feasibility of the problems vary. We show that either convergence or diversity can be emphasized by contracting or expanding the dominance area. Also, we show that the optimal value of the area of dominance depends strongly on all factors analyzed here: number of objectives, size of the search space, and feasibility of the problems.
Scientific journal, English - Local Dominance and Local Recombination in MOEAs on 0/1 Multiobjective Knapsack Problems
Hiroyuki Sato; Hernán Aguirre; Kiyoshi Tanaka
Lead, EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, ELSEVIER SCIENCE BV, 181, 3, 1708-1723, 2007, Peer-reviwed, This work studies and compares the effects on performance of local dominance and local recombination applied with different locality in multiobjective evolutionary algorithms on combinatorial 0/1 multiobjective knapsack problems. For this purpose, we introduce a method that creates a neighborhood around each individual and assigns a local dominance rank after alignment of the principle search direction of the neighborhood by using polar coordinates in objective space. For recombination a different neighborhood determined around a random principle search direction is created. The neighborhood sizes for dominance and recombination are separately controlled by two different parameters. Experimental results show that the optimum locality of dominance is different from the optimum locality of recombination. Additionally, it is shown that the performance of the algorithm that applies local dominance and local recombination with different locality is significantly better than the performance of algorithms applying local dominance alone, local recombination alone, or dominance and recombination globally as conventional approaches do. (C) 2006 Elsevier B.V. All rights reserved.
Scientific journal, English - Effect of Controlling Dominance Area of Solutions in MOEAs on Convex Problems with Many Objectives
Hiroyuki Sato; Hernán Aguirre; Kiyoshi Tanaka
The 7th International Conference on Optimization: Techniques and Applications (ICOTA7), in CD-ROM, 1-10, 2007, Peer-reviwed
International conference proceedings, English - On the Locality of Dominance and Recombination in Multiobjective Evolutionary Algorithms
Hiroyuki Sato; Hernán Aguirre; Kiyoshi Tanaka
2005 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-3, PROCEEDINGS, IEEE, 451-458, 2005, Peer-reviwed, This work studies and compares the effects on performance of local dominance and local recombination applied with different locality in multiobjective evolutionary algorithms on combinatorial multiobjective problems. For this purpose, we introduce a method that creates a neighborhood around each individual and assigns a local dominance rank after rotating the principal search direction of the neighborhood by using polar coordinates in objective space. For recombination a different neighborhood determined around a random principle search direction is created. The neighborhood sizes for dominance and recombination are separately controlled by two different parameters. Experimental results show that the optimum locality of dominance is different from the optimum locality of recombination. Additionally, it is shown that the performance of the algorithm that applies local dominance and local recombination with different locality is significantly better than the performance of algorithms applying local dominance alone, local recombination alone, or dominance and recombination globally as conventional approaches do.
International conference proceedings, English - Enhanced Multi-objective Evolutionary Algorithms Using Local Dominance
Hiroyuki Sato; Hernán Aguirre; Kiyoshi Tanaka
Lead, Proc. International Workshop on Nonlinear Circuits and Signal Processing (NCSP2004), in CD-ROM, 319-322, 2004, Peer-reviwed
International conference proceedings, English - Local Dominance Using Polar Coordinates to Enhance Multiobjective Evolutionary Algorithms
Hiroyuki Sato; Hernán Aguirre; Kiyoshi Tanaka
CEC2004: PROCEEDINGS OF THE 2004 CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1 AND 2, IEEE, 188-195, 2004, Peer-reviwed, In this paper, we propose a calculation method of local dominance and enhance multiobjective evolutionary algorithms by performing a distributed search based on local dominance. In this method, we first transform all fitness vectors of individuals to polar coordinate vectors in the objective function space. Then we divide the population into several sub-populations by using declination angles. We calculate local dominance for individuals belonging to each sub-population based on the local search direction, and apply selection, recombination, and mutation to individuals within each sub-population. We pick up NSGA-II and SPEA2 as two representatives of the latest generation of multiobjective evolutionary algorithms and enhance them with our method. We verify the effectiveness of the proposed method obtaining Pareto optimal solutions satisfying diversity conditions by comparing the search performance between the conventional algorithms and their enhanced versions.
International conference proceedings, English - Effects from Local Dominance and Local Recombination in Enhanced MOEAs
Hiroyuki Sato; Hernán Aguirre; Kiyoshi Tanaka
Proc. 5th International Conference on Simulated Evolution and Learning (SEAL2004), in CD-ROM, 1-6, 2004, Peer-reviwed
International conference proceedings, English
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- パレートフロントのアンサンブル推定に関する検討
木川田 幸翼; 奥村 成; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 第25回進化計算学会研究会, pp. 169--178
2024
21 Mar. 2024- 22 Mar. 2024 - 解集合アグリゲーションのためのパレートフロントモデルの最適化―テスト問題とビル運用最適化問題における検証―
奥村 成; 太田 恵大; 佐藤 冬樹; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 第25回進化計算学会研究会, pp. 158--168
2024
21 Mar. 2024- 22 Mar. 2024 - ⼤脳新⽪質学習に基づくサッカード運動の模倣に関する検討
松⽥優⼀; 丹⽻和磨; ⾼⽟圭樹; 佐藤寛之
Oral presentation, Japanese, 第51回 知能システムシンポジウム，計測自動制御学会 システム・情報部門, pp. 66--71
2024
11 Mar. 2024- 12 Mar. 2024 - ⼤脳新⽪質学習に基づく多変量の時系列予測の補完に関する検討
丹⽻和磨; 藤野和志; ⾼⽟圭樹; 佐藤寛之
Oral presentation, Japanese, 第51回 知能システムシンポジウム，計測自動制御学会 システム・情報部門, pp. 61--65
2024
11 Mar. 2024- 12 Mar. 2024 - Sustainable Real-world Applications Through Evolutionary Multi/Many-objective Optimization
Hiroyuki Sato
Keynote oral presentation, English, The 14th International Conference on Cloud Computing, Data Science & Engineering (Confluence 2024), Invited
2024
18 Jan. 2024- 19 Jan. 2024 - パレート局所解ネットワークの近似的な構築
田中 彰一郎; Gabriela Ochoa; Arnaud Liefooghe; 髙玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 進化計算シンポジウム2023, pp. 224--233
2023
21 Dec. 2023- 22 Dec. 2023 - 制約付き多目的最適化のためのプッシュ・プル探索における指向性交配
高宮 諒翔; 宮川 みなみ; 髙玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 進化計算シンポジウム2023, pp. 19--26
2023
21 Dec. 2023- 22 Dec. 2023 - タスク間類似度を用いる制約付き進化多因子最適化に関する一検討
川上 紫央; 髙玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 進化計算シンポジウム2023, pp. 338--345
2023
21 Dec. 2023- 22 Dec. 2023 - 多目的進化計算における非劣解アーカイブからの親集合の再選択に関する検討
佐藤 和磨; 奥村 成; 髙玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 進化計算シンポジウム2023, pp. 151--157
2023
21 Dec. 2023- 22 Dec. 2023 - 洋上の航空路への合流へ向けたロバストスケジューリング
石塚 智貴; 佐藤 寛之; 髙玉 圭樹
Oral presentation, Japanese, 進化計算シンポジウム2023, pp. 462--466
2023
21 Dec. 2023- 22 Dec. 2023 - 多目的連続最適化問題における変数間属性を考慮したヘルパー関数の設計
望月 啓吾; 谷津 直弥; 佐藤 寛之; 髙玉 圭樹
Oral presentation, Japanese, 進化計算シンポジウム2023, pp. 441--448
2023
21 Dec. 2023- 22 Dec. 2023 - PSOとCMA-ESの相互支援による動的関数のピーク追従
藤田 翔英; 石澤 竜希; 佐藤 寛之; 髙玉 圭樹
Oral presentation, Japanese, 進化計算シンポジウム2023, pp. 372--379
2023
21 Dec. 2023- 22 Dec. 2023 - 目的関数変形による局所解探索の促進
空閑 智也; 佐藤 寛之; 髙玉 圭樹
Oral presentation, Japanese, 進化計算シンポジウム2023, pp. 354--358
2023
21 Dec. 2023- 22 Dec. 2023 - k近傍照合を用いた進化的ルール学習によるWeighted Prototype Selection
谷津 直弥; 白石 洋輝; 佐藤 寛之; 髙玉 圭樹
Oral presentation, Japanese, 進化計算シンポジウム2023, pp. 309--316
2023
21 Dec. 2023- 22 Dec. 2023 - 非有界探索空間最適化に向けたNovelty based Multi-Objectivizationによる多峰性多点探索
石澤 竜希; 佐藤 寛之; 髙玉 圭樹
Oral presentation, Japanese, 進化計算シンポジウム2023, pp. 250--257
2023
21 Dec. 2023- 22 Dec. 2023 - Evolutionary Multi/Many-objective Optimization and Sustainability-related Real-world Applications
Hiroyuki Sato
Invited oral presentation, English, Fifth International Conference on Computational Intelligence in Communications and Business Analytics (CICBA-2023), Invited
2023
27 Jan. 2023- 28 Jan. 2023 - 車列表現の一般化による多様な車列に適用可能な車両入替手順の進化的最適化
古屋 敬祐; 中理 怡恒; 長濱 章仁; 佐藤 寛之; 髙玉 圭樹
Oral presentation, Japanese, 進化計算学会, 第16回進化計算シンポジウム 2022, pp. 275--282
2022
17 Dec. 2022- 18 Dec. 2022 - 進化的ルール学習と次元削減・生成モデルのハイブリッドモデルにおける観測空間での報酬による潜在空間を経由したルール学習手法
谷津 直弥; 白石 洋輝; 佐藤 寛之; 髙玉 圭樹
Oral presentation, Japanese, 進化計算学会, 第16回進化計算シンポジウム 2022, pp. 169--176
2022
17 Dec. 2022- 18 Dec. 2022 - 少数個体に向けた局所解アーカイブに基づく局所解探索のための複数粒子群最適化
前川 裕介; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 第16回進化計算シンポジウム 2022, pp. 316--323
2022
17 Dec. 2022- 18 Dec. 2022 - 無限探索空間のマルチモーダル最適化問題に向けた粒子群最適化による動的探索範囲拡張
石澤 竜希; 空閑 智也; 前川 祐介; 佐藤 寛之; 高玉圭樹
Oral presentation, Japanese, 第16回進化計算シンポジウム 2022, pp. 153--160
2022
17 Dec. 2022- 18 Dec. 2022 - 進化計算による多目的在庫配置最適化における重みベクトル群の偏向配置
佐藤和磨; 三井康行; 川上紫央; 田中彰一郎; 佐藤寛之
Oral presentation, Japanese, 進化計算シンポジウム2022, pp. 345--352
2022
17 Dec. 2022- 18 Dec. 2022 - 多因子CNKランドスケープ問題において類似目的関数を利用する進化計算
川上 紫央; 田中 彰一郎; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 進化計算シンポジウム2022, pp. 294--302
2022
17 Dec. 2022- 18 Dec. 2022 - マルチファネル構造を持つ単一目的最適化問題の多目的化による緩和
田中彰一郎; 高玉圭樹; 佐藤寛之
Oral presentation, Japanese, 進化計算シンポジウム2022, pp. 54--62
2022
17 Dec. 2022- 18 Dec. 2022 - 二個体協調における自由度に基づくマルチエージェント逆強化学習
植木 駿介; 亀谷 長太; 戸板 佳祐; 中理 怡恒; 佐藤 寛之; 髙玉 圭樹
Oral presentation, Japanese, 計測自動制御学会, システム・情報部門 学術講演会 2022 (SSI2022), pp. 328--333
2022
25 Nov. 2022- 27 Nov. 2022 - 反復的に推定モデルを更新する教師あり多目的最適化アルゴリズムに関する検討
高木智章,髙玉圭樹,佐藤寛之
Oral presentation, Japanese, システム・情報部門学術講演会 (SSI2022), 計測自動制御学会, pp. 260--265
2022
25 Nov. 2022- 27 Nov. 2022 - 進化計算による実世界システムの最適化
佐藤寛之
Invited oral presentation, Japanese, 計測自動制御学会 システム・情報部門 学術講演会 SSI2022, Invited
2022
25 Nov. 2022- 27 Nov. 2022 - 大脳新皮質学習アルゴリズムにおけるシナプスの適応配置とカラムに基づくデコーダの協調
青木 健; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, システム・情報部門学術講演会2022 (SSI2022)，計測自動制御学会, pp. 177--182, Domestic conference
2022
25 Nov. 2022- 27 Nov. 2022 - 多目的化が最適化に与える影響に関する基礎的検討
田中 彰一郎; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, システム・情報部門学術講演会2022 (SSI2022)，計測自動制御学会, pp. 276--279, Domestic conference
2022
25 Nov. 2022- 27 Nov. 2022 - 多因子離散最適化問題において目的関数の類似度を計測して利用する進化計算の効果
川上 紫央; 田中 彰一郎; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, システム・情報部門学術講演会2022 (SSI2022)，計測自動制御学会, pp. 270--275, Domestic conference
2022
25 Nov. 2022- 27 Nov. 2022 - 大脳新皮質学習における適応的なシナプス調整と予測値デコードの効果
藤野 和志; 青木 健; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, システム・情報部門学術講演会2022 (SSI2022)，計測自動制御学会, pp. 160--165, Domestic conference
2022
25 Nov. 2022- 27 Nov. 2022 - 大脳新皮質学習における抑制性セルの導入と効果
後藤 祐希; 青木 健; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, システム・情報部門学術講演会2022 (SSI2022)，計測自動制御学会, pp. 166--170, Domestic conference
2022
25 Nov. 2022- 27 Nov. 2022 - 多数目的進化計算によるオフィスビルの制御最適化における解集合のアーカイブと再活用
奥村成; 太田恵大; 佐藤寛之
Oral presentation, Japanese, 第30回インテリジェント・システム・シンポジウム FAN2022，計測自動制御学会 システム・情報部門, pp. 300--304, Domestic conference
2022
21 Sep. 2022- 22 Sep. 2022 - 多目的進化計算を用いた商品在庫最適化における商品別および倉庫別交叉の有効性検証
三井 康行; 山越 悠貴; 佐藤 寛之
Oral presentation, Japanese, 人工知能学会全国大会2022, pp. 1--4, Domestic conference
2022
14 Jun. 2022- 17 Jun. 2022 - 多変量大脳新皮質学習アルゴリズムのための適応的シナプス調整
藤野和志; 青木健; 高玉圭樹; 佐藤寛之
Oral presentation, Japanese, 第20回コンピューテーショナル・インテリジェンス研究会, 計測自動制御学会, pp. 53--60, Domestic conference
2022
03 Jun. 2022- 04 Jun. 2022 - 反復的局所探索を用いた経済支援施策の設計最適化
高木智章; 髙玉圭樹; 佐藤寛之
Oral presentation, Japanese, 第21回進化計算学会研究会, pp. 28--35
2022
17 Mar. 2022- 18 Mar. 2022 - 渋滞緩和に向けた交通流率と車列の不安定性を考慮した進化計算よる車両順最適化
古屋 敬祐; 中理 怡恒; 河野 航大; 長濱 章仁; 佐藤 寛之; 髙玉 圭樹
Oral presentation, Japanese, 計測自動制御学会, 第49回知能システムシンポジウム, B6-2
2022
14 Mar. 2022- 14 Mar. 2022 - 覚醒とNon-REM睡眠の影響を除去した体動の出現頻度に基づく非拘束型REM睡眠推定
嘉村 魁人; 松田 尚也; 千住 太希; 中理 怡恒; 佐藤 寛之; 髙玉 圭樹
Oral presentation, Japanese, 計測自動制御学会, 第49回知能システムシンポジウム, A2-4
2022
14 Mar. 2022- 14 Mar. 2022 - 進化型多目的最適化における重みベクトル選択に基づく解集団分割と領域別探索
河野 航大; 髙玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 進化計算学会, 第15回進化計算シンポジウム2021, pp. 305--312
2021
25 Dec. 2021- 26 Dec. 2021 - β分布に基づく学習分類子システム
白石 洋輝; 速水 陽平; 佐藤 寛之; 髙玉 圭樹
Oral presentation, Japanese, 進化計算学会, 第15回進化計算シンポジウム2021, pp. 218--225
2021
25 Dec. 2021- 26 Dec. 2021 - VAEを用いた学習分類子システムによる高次元マルチステップ問題の汎用的ルール学習
谷津 直弥; 白石 洋輝; 佐藤 寛之; 髙玉 圭樹
Oral presentation, Japanese, 進化計算学会, 第15回進化計算シンポジウム2021, pp. 196--202
2021
25 Dec. 2021- 26 Dec. 2021 - 複数解探索のための収束状況に応じた複数群間移動に基づく実ロボット適用に向けた粒子群最適化
前川 裕介; 河野 航大; 佐藤 寛之; 髙玉 圭樹
Oral presentation, Japanese, 進化計算学会, 第15回進化計算シンポジウム 2021, pp. 64--71
2021
25 Dec. 2021- 26 Dec. 2021 - 遺伝的アルゴリズムの複数の実問題に対する概念実証のプログラム作成を通して 進化計算ソフトウェア開発のビジネス展開の検討
藤原博文; 高木智章; 佐藤寛之
Oral presentation, Japanese, 進化計算シンポジウム2021, pp. 43--50, Domestic conference
2021
25 Dec. 2021- 26 Dec. 2021 - デジタルツインを用いたビル設備制御設計の制約付き多数目的最適化
福原 洸平; 熊谷 涼; 川野 裕希; 太田 恵大; 佐藤 寛之
Oral presentation, Japanese, 進化計算シンポジウム2021, pp. 141--148, Domestic conference
2021
25 Dec. 2021- 26 Dec. 2021 - 多目的進化計算によるECにおける商品在庫配置最適化
三井 康行; 山越 悠貴; 佐藤 寛之
Oral presentation, Japanese, 進化計算シンポジウム2021, pp. 226--233, Domestic conference
2021
25 Dec. 2021- 26 Dec. 2021 - 単一の目的関数のランドスケープが多目的最適化に与える影響
田中彰一郎; 高玉圭樹; 佐藤 寛之
Oral presentation, Japanese, 進化計算シンポジウム2021, pp. 260--268, Domestic conference
2021
25 Dec. 2021- 26 Dec. 2021 - 3目的最適化結果の可視化法に関する比較検討
高木智章; 高玉圭樹; 佐藤寛之
Oral presentation, Japanese, 進化計算シンポジウム2021, pp. 336--345, Domestic conference
2021
25 Dec. 2021- 26 Dec. 2021 - 実数値学習分類子システムにおける最近傍ルールの汎化レベルを継承する被覆法
白石 洋輝; 速水 陽平; 佐藤 寛之; 髙玉 圭樹
Oral presentation, Japanese, 計測自動制御学会, システム・情報部門 学術講演会 2021 (SSI2021), pp. 434--439
2021
21 Nov. 2021- 22 Nov. 2021 - ルールの過剰汎化率を考慮したAbsumptionに基づく実数値学習分類子システム
白石 洋輝; 速水 陽平; 佐藤 寛之; 髙玉 圭樹
Oral presentation, Japanese, 計測自動制御学会, 第30回インテリジェント・システム・シンポジウム (FAN 2021), pp. 143--148
2021
21 Sep. 2021- 23 Sep. 2021 - グリッド型多目的進化計算における可変エリート集団に関する検討
加納謙介; 高木智章; 田中彰一郎; 髙玉圭樹; 佐藤寛之
Oral presentation, Japanese, インテリジェント・システム・シンポジウム 2021 (FAN 2021), pp. 268--273, Domestic conference
2021
21 Sep. 2021- 23 Sep. 2021 - 多目的意思決定を支援する有向パレートフロントの推定
高木智章; 田中彰一郎; 髙玉圭樹; 佐藤寛之
Oral presentation, Japanese, インテリジェント・システム・シンポジウム 2021 (FAN 2021), pp. 42--50, Domestic conference
2021
21 Sep. 2021- 23 Sep. 2021 - 吸収マルコフ連鎖に基づく局所解ネットワークに関する検討
田中彰一郎; 古谷博史; 日和悟; 廣安知之; 高玉圭樹; 佐藤寛之
Oral presentation, Japanese, 第20回進化計算学会研究会, pp. 34--41, Domestic conference
2021
08 Sep. 2021- 09 Sep. 2021 - 推定を利用する教師あり多目的最適化アルゴリズムに関する検討
高木智章; 高玉圭樹; 佐藤寛之
Oral presentation, Japanese, 第20回進化計算学会研究会, pp. 42--50, Domestic conference
2021
08 Sep. 2021- 09 Sep. 2021 - 目的関数の類似度を利用した多因子進化計算に関する検討
川上紫央; 高玉圭樹; 佐藤寛之
Oral presentation, Japanese, 第20回進化計算学会研究会, pp. 74--81, Domestic conference
2021
08 Sep. 2021- 09 Sep. 2021 - 多目的進化計算によるオフィスビルの空調設定スケジュールの最適化―数理モデルからシミュレーションベース, ロバスト, サロゲート最適化への展開―
太田 恵大; 佐藤 寛之
Oral presentation, Japanese, 第17回進化計算学会研究会, pp. 194--204
2020
08 Sep. 2021- 09 Sep. 2021 - 多目的最適化の最前線，活用法と課題
Hiroyuki Sato
Keynote oral presentation, Japanese, 第15回 System Simulation Symposium, Invited
2021
09 Jul. 2021- 09 Jul. 2021 - 時系列予測に基づく行動決定のための大脳新皮質学習に関する基礎検討
藤野和志; 青木健; 高玉圭樹; 佐藤寛之
Oral presentation, Japanese, 第17回コンピューテーショナル・インテリジェンス研究会，システム・情報部門，計測自動制御学会, pp. 63--67, Domestic conference
2021
25 Mar. 2021- 26 Mar. 2021 - 正しい意見共有に向けたユーザの投稿頻度を考慮したエージェントネットワークシステム：人とエージェントの関係から人とエージェント集団の関係への展開
山根 大輝; 前川 佳幹; 荒井 亮太郎; 福本 有季子; 佐藤 寛之; 髙玉 圭樹
Oral presentation, Japanese, 人工知能学会, HAIシンポジウム2021, G-17
2021
09 Mar. 2021- 10 Mar. 2021 - 睡眠段階ごとの生体振動特徴に着目したニューラルネットワークによる推定
千住 太希; 中理 怡恒; 佐藤 寛之; 髙玉 圭樹
Oral presentation, Japanese, 計測自動制御学会, 第48回知能システムシンポジウム, A5-4
2021
08 Mar. 2021- 09 Mar. 2021 - 心拍数から推定した概日/非概日リズムの振幅比率に基づくアルツハイマー型認知症判定
松田 尚也; 荒井 亮太郎; 藁谷 由香; 中理 怡恒; 佐藤 寛之; 髙玉 圭樹; 廣瀬雅宣; 長谷川 洋; 白石 眞; 松田 隆秀
Oral presentation, Japanese, 計測自動制御学会, 第48回知能システムシンポジウム, A-51
2021
08 Mar. 2021- 09 Mar. 2021 - 大脳新皮質学習における多層化に関する検討
青木健; 高玉圭樹; 佐藤寛之
Oral presentation, Japanese, 計測自動制御学会 第48回 知能システムシンポジウム, pp. 1--5, Domestic conference
2021
08 Mar. 2021- 09 Mar. 2021 - VAEの潜在変数空間における分布ベース照合に基づく学習分類子システム
田所 優和; 佐藤 寛之; 髙玉 圭樹
Oral presentation, Japanese, 計測自動制御学会, システム・情報部門 学術講演会 2020 (SSI2020), pp. 451--456
2020 - 次元削減ルールの復元における過剰一般化と被覆における過剰特化を回避するXCS による高次元ルールの解釈性向上
田所 優和; 佐藤 寛之; 髙玉 圭樹
Oral presentation, Japanese, 進化計算学会, 第14回進化計算シンポジウム 2020, pp. 219--226
2020 - 学習分類子システムのルール進化に対するConditional VAE に基づく誤判定訂正
白石 洋輝; 田所 優和; 速水 陽平; 福本 有季子; 佐藤 寛之; 髙玉 圭樹
Oral presentation, Japanese, 進化計算学会, 第14回進化計算シンポジウム 2020, pp. 211--218
2020 - 重みベクトルと混雑距離における解選択の相互補完に基づく進化計算:MOEA/D とNSGA-II の融合
河野 航大; 梶原 奨; 田所 優和; 佐藤 寛之; 髙玉 圭樹
Oral presentation, Japanese, 進化計算学会, 第14回進化計算シンポジウム 2020, pp. 168--175
2020 - 複数局所解探索のための複数群間移動に基づく粒子群最適化：実ロボット環境に向けた個体数固定と同時移動への展開
前川 裕介; 河野 航大; 梶原 奨; 福本 有季子; 佐藤 寛之; 髙玉 圭樹
Oral presentation, Japanese, 進化計算学会, 第14回進化計算シンポジウム 2020, pp. 88--95
2020 - 大脳新皮質学習の階層化における抑制フィードバックの検討
青木健; 高玉圭樹; 佐藤寛之
Oral presentation, Japanese, 計測自動制御学会 システム・情報部門 学術講演会 2020, pp. 1--6, Domestic conference
2020 - 仮想目的ベクトル群によるパレートフロントの形状推定
高木智章; 高玉圭樹; 佐藤寛之
Oral presentation, Japanese, 第18回進化計算学会研究会, pp. 46--54, Domestic conference
2020 - 大脳新皮質学習におけるカラムに基づく予測表現デコーダに関する検討
青木健; 高玉圭樹; 佐藤寛之
Oral presentation, Japanese, 計測自動制御学会 システム・情報部門 学術講演会 2020, pp. 130--135, Domestic conference
2020 - 多変量大脳新皮質学習によるデータの連続欠損に対する予測持続に関する検討
長島晶彦; 青木健; 高玉圭樹; 佐藤寛之
Oral presentation, Japanese, 計測自動制御学会 システム・情報部門 学術講演会 2020, pp. 124--129, Domestic conference
2020 - MarioGANと進化計算による多様なステージ生成に関する検討
熊谷涼; 高木智章; 高玉圭樹; 佐藤寛之
Oral presentation, Japanese, 計測自動制御学会 システム・情報部門 学術講演会 2020, pp. 56--61, Domestic conference
2020 - 目的関数空間の単位超平面を基準とするパレートフロント推定とその利用
高木智章; 高玉圭樹; 佐藤寛之
Oral presentation, Japanese, 進化計算シンポジウム2020, pp. 35--43, Domestic conference
2020 - 最適化問題の類似性を利用した未知の最適化問題に適した進化計算法の推薦
山本康平; 高木智章; 髙玉圭樹; 佐藤寛之
Oral presentation, Japanese, 進化計算シンポジウム2020, pp. 249--256, Domestic conference
2020 - 多目的空調設定スケジュール最適化問題における局所的な多峰性に関する考察
太田恵大; 佐藤寛之
Oral presentation, Japanese, 進化計算シンポジウム2020, pp. 145--150, Domestic conference
2020 - 制約許容量を導入した指向性交配による制約付き多目的最適化の一検討
宮川みなみ; 佐藤寛之; エルナンアギレ; 田中清
Oral presentation, Japanese, 進化計算シンポジウム2020, pp. 241--248, Domestic conference
2020 - 多目的意思決定支援のためのパレートフロントの上位集合の獲得に関する検討
高木智章; 高玉圭樹; 佐藤寛之
Oral presentation, Japanese, 第17回進化計算学会研究会，進化計算学会, pp. 40--47, Domestic conference
2020 - 評価値軸・設計変数上の解の継続変化に対する群知能アルゴリズムのメカニズムの設計とその追従性評価
高野 諒; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 進化計算シンポジウム2019，進化計算学会, pp. 177--184, Domestic conference
2019 - 不確実性を伴うデータを分類するルール獲得に向けた正確性によるルール選択メカニズムの設計
辰巳嵩豊; 佐藤寛之; 高玉 圭樹
Oral presentation, Japanese, 進化計算シンポジウム2019，進化計算学会, pp. 289--296, Domestic conference
2019 - 局所収束回避に向けた粒子群最適化と差分進化の空間的探索戦略の切り替え
河野 航大; 梶原 奨; 小林 亮太; 高野 諒; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 進化計算シンポジウム2019，進化計算学会, pp. 381--388, Domestic conference
2019 - バス路線網における運行形態の一般化による環境変化への適応
梶原 奨; 村田 暁紀; 長谷川 智; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 第46回知能システムシンポジウム, pp. 1--6, Domestic conference
2019 - 分解型多数目的最適化における指向確率選択と二重連鎖更新の効果
佐藤寛之; 高玉圭樹
Oral presentation, Japanese, 進化計算シンポジウム2019，進化計算学会, pp. 352--360, Domestic conference
2019 - LSTMを用いた近似モデルによる空調設定スケジュールの進化型最適化
太田恵大; 笹川隆史; 佐藤寛之
Oral presentation, Japanese, 進化計算シンポジウム2019，進化計算学会, pp. 13--20, Domestic conference
2019 - 非劣解サンプリングのための多目的進化計算における環境選択法の比較評価
高木智章; 高玉圭樹; 佐藤寛之
Oral presentation, Japanese, 進化計算シンポジウム2019，進化計算学会, pp. 143--151, Domestic conference
2019 - 格子型多数目的進化アルゴリズムにおける分解粒度の適応的決定に関する検討
加納謙介; 高木智章; 髙玉圭樹; 佐藤寛之
Oral presentation, Japanese, 進化計算シンポジウム2019，進化計算学会, pp. 201--205, Domestic conference
2019 - 多目的最適化のベンチマーク問題マップとアルゴリズムマップに関する検討
山本康平; 高木智章; 髙玉圭樹; 佐藤寛之
Oral presentation, Japanese, 進化計算シンポジウム2019，進化計算学会, pp. 264--268, Domestic conference
2019 - 多因子距離最小化問題における進化計算の性能比較
川上紫央; 高木智章; 高玉圭樹; 佐藤寛之
Oral presentation, Japanese, 進化計算シンポジウム2019，進化計算学会, pp. 278--283, Domestic conference
2019 - 競争群最適化における比較群サイズが最適化性能に与える影響
三好陵太; 高木智章; 髙玉圭樹; 佐藤寛之
Oral presentation, Japanese, 進化計算シンポジウム2019，進化計算学会, pp. 361--365, Domestic conference
2019 - 自己構成型大脳新皮質学習における時間遅れシナプスの検討
鈴ヶ嶺 聡哲; 青木 健; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 計測自動制御学会 第46回 知能システムシンポジウム, pp. 1--6, Domestic conference
2019 - 大脳新皮質学習における適応型シナプス配置法の検討
青木 健; 鈴ヶ嶺 聡哲; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 計測自動制御学会 第46回 知能システムシンポジウム, pp. 1--6, Domestic conference
2019 - 多目的最適化問題の目的関数空間における解の存在域に関する分析
高木智章; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 第15回進化計算学会研究会, pp. 42--47, Domestic conference
2019 - 進化計算による多目的最適化における 重みベクトル群の適応配置に関する基礎検討
高木智章; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 第16回進化計算学会研究会, pp. 28--32, Domestic conference
2019 - 重みベクトルの部分集合選択による進化型多目的最適化に関する基礎検討
高木智章; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 電子・情報・システム部門大会, 電気学会, pp. 1060--1065, Domestic conference
2019 - 大脳新皮質学習における異なる時系列データの複合予測に関する基礎検討
長島晶彦; 青木 健; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 計測自動制御学会（SICE），システム・情報部門，第15回コンピューテーショナル・インテリジェンス研究会, pp. 5--12, Domestic conference
2019 - 解釈可能なルール獲得に向けた深層生成モデルによる次元削減に基づく学習分類子システム
田所 優和; 速水 陽平; 藤野 貴章; 辰巳 嵩豊; 佐藤 寛之; 髙玉 圭樹
Oral presentation, Japanese, 進化計算学会, 第12回進化計算シンポジウム 2018, pp. 319--325
2018 - 動的最適化問題に向けた異種戦略の個体別適用に基づく差分進化
小林 亮太; 岩瀬 拓哉; 高野 諒; 佐藤 寛之; 髙玉 圭樹
Oral presentation, Japanese, 進化計算学会, 第12回進化計算シンポジウム 2018, pp. 284--290
2018 - 脳新皮質学習におけるカラムとセルの動的構成に関する検討
鈴ヶ嶺 聡哲; 青木 健; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 第117回数理モデル化と問題解決(MPS)研究会報告, 情報処理学会, pp. 1--2, Domestic conference
2018 - 自己構成型の大脳新皮質学習アルゴリズムに関する検討
鈴ヶ嶺 聡哲; 青木 健; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 計測自動制御学会（SICE），システム・情報部門，第13回コンピューテーショナル・インテリジェンス研究会, pp. 51--58, Domestic conference
2018 - 多目的進化計算における重みベクトルの分布に関する基礎検討
高木智章; 高玉圭樹; 佐藤寛之
Oral presentation, Japanese, 第28回インテリジェント・システム・シンポジウム, pp. 165--170, Domestic conference
2018 - 大脳新皮質学習におけるシナプスの動的再配置に関する検討
青木 健; 鈴ヶ嶺 聡哲; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 計測自動制御学会 システム・情報部門 学術講演会 2018, GS04-18, Domestic conference
2018 - 大脳新皮質学習におけるカラムとセルの自己構成法の動作解析
鈴ヶ嶺 聡哲; 青木 健; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 計測自動制御学会 システム・情報部門 学術講演会 2018, GS04-19, Domestic conference
2018 - Evolutionary Many-Objective Optimization: Difficulties and Approaches
Hiroyuki Sato
Oral presentation, Japanese, 2018 JPNSEC International Workshop on Evolutionary Computation, International conference
2018 - 航空機着陸問題におけるクラスタリングを用いた分割反復最適化手法
村田 暁紀; 佐藤 寛之; 高玉 圭樹; Daniel Delahaye
Oral presentation, Japanese, 電気学会，第28回インテリジェント・システム・シンポジウム, pp. 167--172, Domestic conference
2018 - 負の報酬生成による環境変化に適応可能な逆強化学習
長谷川 智; 梅内 祐太; 上野 史; 佐藤 寛之; 山口 智浩; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，第45回知能システムシンポジウム, C4-2, Domestic conference
2018 - 帰宅困難者の滞留解消に向けた区間混雑に基づく路線間バス譲渡
高谷 美穂; 石井 晴之; 張 財立; 辰巳 嵩豊; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，第45回知能システムシンポジウム, A5-3, Domestic conference
2018 - 最適化問題群マップにおける未知問題の位置推定に関する基礎検討
山本 康平; 宮川みなみ; 高玉圭樹; 佐藤寛之
Oral presentation, Japanese, 計測自動制御学会 システム・情報部門 学術講演会 2018, SS14-13, Domestic conference
2018 - 分解に基づく多目的進化計算における重みベクトル群の分布制御に関する検討
高木智章; 高玉圭樹; 佐藤寛之
Oral presentation, Japanese, 進化計算学会進化計算シンポジウム2018, pp. 160--167, Domestic conference
2018 - 多目的最適化問題群マップにおける未知問題の位置推定法の検討
山本 康平; 宮川みなみ; 高玉圭樹; 佐藤寛之
Oral presentation, Japanese, 進化計算学会進化計算シンポジウム2018, pp. 360--366, Domestic conference
2018 - オフィスビル空調スケジュールの進化型多目的最適化に関する検討
太田恵大; 佐藤寛之
Oral presentation, Japanese, 進化計算学会進化計算シンポジウム2018, pp. 184--191, Domestic conference
2018 - 覚醒と浅睡眠に着目した圧力センサに基づく非侵襲的睡眠段階推定とその精度向上
上原 知里; 松本 和馬; 田島 友祐; 小峯 嵩裕; 原田 智広; 佐藤 寛之; 髙玉 圭樹
Oral presentation, Japanese, 計測自動制御学会, 第44回知能システムシンポジウム, A3-3
2017 - 難易度と技術偏差に基づく学習目標生成を促すインタラクティブ学習支援
福田 千賀; 村田 暁紀; 石井 晴之; 佐藤 寛之; 髙玉 圭樹
Oral presentation, Japanese, 計測自動制御学会, 第44回知能システムシンポジウム, A3-2
2017 - 最適性と多様性のトレードオフを考慮したノベルティサーチに基づく多目的進化計算
村田 暁紀; 佐藤 寛之; 髙玉 圭樹
Oral presentation, Japanese, 情報処理学会, 第112回数理モデル化と問題解決研究発表会, pp. 1--6
2017 - 指向性交配を用いるMOEA/Dの制約付き多数目的最適化
宮川 みなみ; 佐藤 寛之; 佐藤 裕二
Oral presentation, Japanese, 進化計算シンポジウム2017, pp. 124--130
2017 - 深層学習による圧縮ルールを復元する学習分類子システムとその精度向上
松本 和馬; 高野 諒; 佐藤 寛之; 髙玉 圭樹
Oral presentation, Japanese, 第13回進化計算学会研究会, 進化計算学会, pp. 98--101
2017 - 環境変化に向けたPSOとCuckoo Searchに基づく解集団混合進化計算
梅内 祐太; 上野 史; 佐藤 寛之; 髙玉 圭樹
Oral presentation, Japanese, 第13回進化計算学会研究会, 進化計算学会, pp. 46--48
2017 - 適応的局所情報共有範囲に基づくArtificial Bee Colonyアルゴリズムによる動的多峰性関数最適化
高野 諒; 佐藤 寛之; 髙玉 圭樹
Oral presentation, Japanese, 第13回進化計算学会研究会, 進化計算学会, pp. 9--10
2017 - データの曖昧性を許容する学習分類子システム: 介護データのマイニング
藤野 貴章; 辰巳 嵩豊; 張 財立; 松本 和馬; 佐藤 寛之; 髙玉 圭樹
Oral presentation, Japanese, 計測自動制御学会, システム・情報部門 学術講演会 2017 (SSI2017), pp. 544--549
2017 - 複数解探索を考慮した分散型Bat Algorithm
岩瀬 拓哉; 高野 諒; 上野 史; 梅内 祐太; 石井 晴之; 佐藤 寛之; 髙玉 圭樹
Oral presentation, Japanese, 計測自動制御学会, システム・情報部門 学術講演会 2017 (SSI2017), pp. 534--539
2017 - 動的環境適応に向けた粒子群最適化とカッコウ探索の協働のための情報共有方法の検討
梅内 祐太; 上野 史; 佐藤 寛之; 髙玉 圭樹
Oral presentation, Japanese, 進化計算学会, 第11回進化計算シンポジウム2017, pp. 455--461
2017 - Searching Multiple Local Optimal Solutions in Multimodal Function by Bat Algorithm Based on Novelty Search
Takuya Iwase; Ryo Takano; Fumito Uwano; Yuta Umenai; Hiroyuki Sato; Keiki Takadama
Oral presentation, English, 進化計算学会, 第11回進化計算シンポジウム 2017, pp. 318--322
2017 - 多峰性関数におけるランダムな動的環境変化に対する適応的局所情報共有範囲に基づくArtificial Bee Colony アルゴリズムの変化への追従性の検証
高野 諒; 佐藤 寛之; 髙玉 圭樹
Oral presentation, Japanese, 進化計算学会, 第11回進化計算シンポジウム 2017, pp. 176--181
2017 - 深層学習による次元圧縮ルールの学習分類子システムにおける初期ルールとしての可能性
松本 和馬; 高野 諒; 上野 史; 佐藤 寛之; 髙玉 圭樹
Oral presentation, Japanese, 進化計算学会, 第11回進化計算シンポジウム 2017, pp. 168--175
2017 - 不活性セルのシナプス更新による大脳新皮質アルゴリズムの予測精度向上に関する一検討
青木健; 高玉圭樹; 佐藤寛之
Oral presentation, Japanese, 第44回 知能システムシンポジウム，計測自動制御学会, pp. 1--6
2017 - 進化計算のパラメータランキングに基づく多目的最適化問題群のマッピングに関する検討
角口 元章; 宮川 みなみ; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 第13回進化計算学会研究会, pp. 37--45, Domestic conference
2017 - 進化計算による複数車種の同時最適化に関する基礎的検討
佐藤 寛之; 松本 晴佳; 宮川 みなみ; 田中 麻莉子; 佐藤 未来子; 佐藤 裕二
Oral presentation, Japanese, 第13回進化計算学会研究会, pp. 89--97, Domestic conference
2017 - 進化計算による多目的集合パッキング最適化における実行不可能解の二段階修復法
田中麻莉子; 山岸雄樹; 永井秀稔; 佐藤寛之
Oral presentation, Japanese, 計測自動制御学会, 第60回自動制御連合講演会, SaJ1-4, Domestic conference
2017 - 進化計算による複数車種の同時最適化における複写オペレータの効果
佐藤 寛之; 松本 晴佳; 宮川 みなみ; 田中 麻莉子; 佐藤 未来子; 佐藤 裕二
Oral presentation, Japanese, 計測自動制御学会 システム・情報部門 学術講演会 2017, pp. 480--484, Domestic conference
2017 - 大脳新皮質アルゴリズムの簡素化と予測精度向上に関する検討
青木 健; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 計測自動制御学会 システム・情報部門 学術講演会 2017, pp. 135--140, Domestic conference
2017 - 目的数が異なる最適化問題群マップの生成に関する検討
角口 元章; 宮川 みなみ; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 進化計算シンポジウム2017, pp. 274--282, Domestic conference
2017 - 多目的集合パッキング問題におけるMOEA/Dの重みベクトル群を活用した実行不可能解の修復法
田中麻莉子; 山岸雄樹; 永井秀稔; 佐藤寛之
Oral presentation, Japanese, 進化計算シンポジウム2017, pp. 404--410, Domestic conference
2017 - 報酬値の分散に基づく学習分類子システムによる一般化度合の異なるルールの同時獲得
ZHANG C; 辰巳嵩豊; 中田雅也; 佐藤寛之; 高玉圭樹
Oral presentation, Japanese, 知能システムシンポジウム講演資料(CD-ROM), A4-4
2016
10 Mar. 2016- 10 Mar. 2016 - Random Forests を活用したLCSによるルールの一般化
土橋 功治; 松本 和馬; 辰巳 嵩豊; 佐藤 寛之; 髙玉 圭樹
Oral presentation, Japanese, 進化計算学会, 第10回進化計算シンポジウム 2016, pp. 332--339
2016 - 学習分類子システムにおける評価回数に基づく分類子の選択圧自動調整
辰巳 嵩豊; Kovacs Tim; 佐藤 寛之; 髙玉 圭樹
Oral presentation, Japanese, 進化計算学会, 第10回進化計算シンポジウム 2016, pp. 138--145
2016 - カッコウ探索に基づく複数のダイナミズムを含む動的環境への適応
梅内 祐太; 上野 史; 佐藤 寛之; 髙玉 圭樹
Oral presentation, Japanese, 進化計算学会, 第10回進化計算シンポジウム 2016, pp. 110--117
2016 - 航空機到着機スケジューリングにおける最適性と多様性のトレードオフを考慮した進化計算
村田 暁紀; 佐藤 寛之; 髙玉 圭樹
Oral presentation, Japanese, 計測自動制御学会, システム・情報部門 学術講演会 2016 (SSI2016), SS02-5
2016 - 半教師あり学習に基づく進化的クラスタリングによる快眠音の個別適応化
星野 秀彰; 村田 暁紀; 建部 尚紀; 中田 雅也; 佐藤 寛之; 髙玉 圭樹
Oral presentation, Japanese, 計測自動制御学会, 第43回知能システムシンポジウム, A4-3
2016 - 次元圧縮に向けた深層学習に基づく学習分類子システム
松本 和馬; 齋藤 嶺; 中田 雅也; 佐藤 寛之; Tim Kovacs; 髙玉 圭樹
Oral presentation, Japanese, 第10回進化計算学会研究会, 進化計算学会, pp. 210--219
2016 - MOEA/Dにおける初期解の連鎖配置法に関する検討
佐藤寛之; 宮川みなみ; 高玉圭樹
Oral presentation, Japanese, 計測自動制御学会 システム・情報部門 学術講演会 2016, pp. 495--500
2016 - Evolutionary Many-objective Optimization
Hiroyuki Sato
Oral presentation, Japanese, Next Generation Transport Aircraft Workshop 2016
2016 - 制約付き多目的最適化のための指向性交配における交叉量操作
宮川みなみ; 佐藤裕二; 高玉圭樹; 佐藤寛之
Oral presentation, Japanese, 計測自動制御学会 システム・情報部門 学術講演会 2016, pp. 501--506
2016 - 最適な進化計算パラメータの差異に基づく多目的最適化問題群のマッピングに関する検討
角口元章; 宮川みなみ; 高玉圭樹; 佐藤寛之
Oral presentation, Japanese, 進化計算シンポジウム2016, pp. 498--504
2016 - 制約付き多目的最適化における指向性交配のための実数値変数交叉法の一検討
宮川みなみ; 佐藤裕二; 佐藤寛之
Oral presentation, Japanese, 進化計算シンポジウム2016, pp. 324--331
2016 - 多目的母材設計における実行不可能解の修復に関する一検討
田中麻莉子; 山岸雄樹; 永井秀稔; 佐藤寛之
Oral presentation, Japanese, 進化計算シンポジウム2016, pp. 146--151
2016 - MOEA/Dにおける解と重みベクトルの親和性に基づく探索量制御に関する一検討
佐藤寛之; 宮川みなみ; 高玉圭樹
Oral presentation, Japanese, 進化計算シンポジウム2016, pp. 518--525
2016 - 平行座標ユーザインターフェースを用いた多数目的選好解集合探索に関する一検討
佐藤 寛之; 冨田 浩平; 宮川 みなみ
Oral presentation, Japanese, 第8回進化計算学会研究会, pp. 100--106
2015 - 報酬分散の収束に依らない分散に基づく学習分類子システム
辰巳 嵩豊; 小峯 嵩裕; 中田 雅也; Tim Kovacs; 佐藤 寛之; 髙玉 圭樹
Oral presentation, Japanese, 進化計算学会 進化計算シンポジウム2015, pp. 262--269
2015 - 多峰性関数における局所探索に基づくCuckoo Search Algorithm
梅内 祐太; 上野 史; 中田 雅也; 佐藤 寛之; 髙玉 圭樹
Oral presentation, Japanese, 進化計算学会 進化計算シンポジウム2015
2015 - MOEA/D における解の連鎖更新法に関する一検討
佐藤 寛之
Oral presentation, Japanese, 進化計算学会 進化計算シンポジウム2015, pp. 233--240
2015 - 多次元ノイズを含む多目的最適化におけるスカラー化関数に基づくロバスト解探索
橋本 知尚; 宮川 みなみ; 髙玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 進化計算学会 進化計算シンポジウム2015, pp. 241--247
2015 - 強化学習環境の規模拡大に対する知識の特殊化による再利用
臼居 浩太郎; 中田 雅也; Tim Kovacs; 佐藤 寛之; 髙玉 圭樹
Oral presentation, Japanese, 計測自動制御学会, システム・情報部門 学術講演会 2015 (SSI2015), pp. 861--865
2015 - 進化計算による多数目的最適化
佐藤寛之
Oral presentation, Japanese, 第8回進化計算学会研究会
2015 - 階層型進化計算を用いた動的航空機着陸経路スケジューリング
村田 暁紀; 森本 紗矢香; 神馬 隆博; 原田 智広; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，第42回知能システムシンポジウム, F-12
2015 - 屋外広告の多目的最適化に関する研究
土斐崎 龍一; 佐藤 寛之; 坂本 真樹
Oral presentation, Japanese, 第14回情報科学技術フォーラム, pp. 361--362
2015 - 制約付き多目的最適化のための指向性交配における解の選出領域の適応制御に関する一検討
宮川 みなみ; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 進化計算シンポジウム 2015, pp. 255--261
2015 - 補助重みベクトル群による多数目的最適化の促進に関する一検討
中川 智; 宮川 みなみ; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 進化計算シンポジウム 2015, pp. 226--232
2015 - ノイズを含む多目的最適化問題におけるマルチレベルのロバスト解探索に関する一検討
橋本知尚; 佐藤寛之
Oral presentation, Japanese, 第6回進化計算学会研究会, pp. 156--161
2014 - 3段式ハイブリッドロケットの概念設計最適化における実行不可能解の有効活用に関する一検討
宮川みなみ; 佐藤寛之; 金森文男; 金崎 雅博
Oral presentation, Japanese, 日本機械学会 第27回計算力学講演会, pp. 217--218
2014 - 分割に基づく進化型多目的最適化における効果的な並列評価法に関する検討
佐藤寛之
Oral presentation, Japanese, 日本機械学会 第27回計算力学講演会, pp. 215-216
2014 - 制約付き多数目的最適化のためのリファレンスラインを用いた指向性交配の検討
宮川みなみ; 高玉圭樹; 佐藤寛之
Oral presentation, Japanese, 進化計算学会 進化計算シンポジウム2014, pp. 363--370
2014 - ノイズを有する多目的最適化問題におけるマルチレベルのロバスト選好解探索法の検討
橋本知尚; 宮川みなみ; 佐藤寛之
Oral presentation, Japanese, 進化計算学会 進化計算シンポジウム2014, pp. 26--33
2014 - 動的環境における局所的情報共有による分散型 ABC アルゴリズム
高野 諒; 市川 嘉裕; 服部 聖彦; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 進化計算学会，第6回進化計算学会研究会, pp. 162--168
2014 - 動的環境おける頑健な確率モデルの学習：ナップサック問題から実問題まで
田島 友祐; 中田 雅也; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 進化計算学会，第6回進化計算学会研究会, pp. 53--59
2014 - 定期便と不定期便の同時獲得型多目的路線網最適化
神馬 隆博; 佐藤 圭二; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 第6回進化計算学会研究会, pp. 1--13
2014 - 時系列行動を評価するパターンマイニングによる外出プラン推薦システム
藤塚 拓馬; 原田 智広; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2014 (SSI2014), pp. 802--807
2014 - 時変環境における局所的情報共有によるArtificial Bee Colonyアルゴリズム
高野 諒; 原田 智広; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 情報処理学会，第102回数理モデル化と問題解決研究発表会
2014 - 不安定報酬環境下における学習分類子システム
辰巳 嵩豊; 小峯 嵩裕; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 第8回進化計算シンポジウム 2014, pp. 99--106
2014 - 超高密度環境下での複数エージェント協調によるデッドロック回避
大谷 雅之; 佐藤 寛之; 服部 聖彦; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会,第40回知能システムシンポジウム, pp. 21--24
2013 - 多目的空間のピボット型一般化による解析
佐藤 圭二; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門，第6回関係論的システム科学調査研究会
2013 - 評価値変動に対応した進化型アルゴリズム
田島 友祐; 中田 雅也; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門，第6回関係論的システム科学調査研究会
2013 - MOEA/Dにおける理想点の高精度化のための探索方向ベクトルの分布制御に関する基礎検討
佐藤寛之
Oral presentation, Japanese, 第五回進化計算学会研究会, pp. 62--67
2013 - 複数制約付き多目的解探索を促進する実行不可能解のアーカイブに関する基礎検討
宮川みなみ; 佐藤寛之
Oral presentation, Japanese, 第五回進化計算学会研究会, pp. 88--93
2013 - 分解に基づく進化型多目的最適化法の効果的な並列化に関する検討
佐藤寛之; 佐藤圭二; 宮川みなみ; Elizabeth Perez-Cortes; 高玉圭樹
Oral presentation, Japanese, 計測自動制御学会 システム・情報部門 学術講演会 2013, pp. 599--603
2013 - Weighted Sum 関数を用いる MOEA/D における解の更新範囲の適応的な決定法に関する検討
佐藤 寛之
Oral presentation, Japanese, 進化計算学会 進化計算シンポジウム2013, pp. 29--36
2013 - 指向性交配における有用な実行不可能解の選出領域制御に関する検討
宮川みなみ; 高玉圭樹; 佐藤 寛之
Oral presentation, Japanese, 進化計算学会 進化計算シンポジウム2013, pp. 22--28
2013 - 可変ピボット型一般化による多様性向上と高速化
佐藤 圭二; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 情報処理学会，第95回数理モデル化と問題解決研究発表会, pp. 1--6
2013 - ピボット型一般化による隅田川河川のロバストな舟運路線網構築
佐藤 圭二; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会，システム・情報部門 学術講演会 2013 (SSI2013), pp. 215--220
2013 - ピボット型一般化に基づく交叉による一般化能力と解探索性能の向上
佐藤 圭二; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 第7回進化計算シンポジウム 2013，進化計算学会, pp. 269--274
2013 - 災害時における道路寸断に対するバス路線網修正と最適化
北川 広登; 佐藤 圭二; 佐藤 寛之; 服部 聖彦; 高玉 圭樹
Oral presentation, Japanese, 進化計算学会 進化計算シンポジウム2013, pp. 156--161
2013 - ナップサック問題における評価値変動に対応した遺伝的アルゴリズムの提案
田島 友祐; 中田 雅也; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 第8回進化計算学会研究会, pp. 1--5
2013 - 異文化体験ゲームにおける集団適応エージェントモデルとインタラクション設計
牛田 裕也; 大谷 雅之; 市川 嘉裕; 佐藤 圭二; 服部 聖彦; 佐藤 寛之; 髙玉 圭樹
Oral presentation, Japanese, 計測自動制御学会, 第39回知能システムシンポジウム, pp. 7--12
2012 - Towards a Distributed Evolutionary P2P Networking Using a Gene Transfer Operation
Elizabeth Pérez-Cortés; Hiroyuki Sato
Oral presentation, English, Evolutionary Computation Symposium 2012 of the Japanese Society for Evolutionary Computation, pp. 359--365
2012 - 環境変化に適応するためのスワップ型一般化
佐藤 圭二; 髙玉 圭樹; 大谷 雅之; 松島 裕康; 市川 嘉裕; 原田 智広; 中田 雅也; 佐藤 寛之; 服部 聖彦
Oral presentation, Japanese, 計測自動制御学会, システム・情報部門 学術講演会 2012 (SSI2012), pp. 335--338
2012 - 環境変化に適応するためのピボット型一般化
佐藤 圭二; 髙玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 第6回進化計算シンポジウム 2012, 進化計算学会, pp. 161--166
2012 - 正確性に基づく学習分類子システムにおける最大個体数の自動調整
松本 隆; 中田 雅也; 佐藤 史盟; 佐藤 圭二; 佐藤 寛之; 服部 聖彦; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会,第39回知能システムシンポジウム, pp. 121--126
2012 - 内部欲求と外部状況の差に基づく目的生成アーキテクチャの設計
金丸 彩乃; 佐藤 圭二; 服部 聖彦; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 計測自動制御学会,第39回知能システムシンポジウム, pp. 45--50
2012 - Evolutionary P2P Networking Enhancing Path-Diversity
Elizabeth Pérez-Cortés; Sho Kawaguchi; Hiroyuki Sato
Oral presentation, English, Proc. 2012 Conv. Record of the Shin-etsu Chapter of IEICE, IEEE Session, pp. 165--165
2012 - 進化型多数目的最適化における解の支配領域の適応的制御
冨田 浩平; 佐藤 寛之
Oral presentation, Japanese, 電子情報学会,平成24年度電子情報通信学会信越支部大会, pp. 129--129
2012 - 進化型多数目的最適化における交叉遺伝子量の動的制御に関する検討
佐藤 寛之; カルロス・コエロ; エルナン・アギレ; 田中 清
Oral presentation, Japanese, 電子情報学会,平成24年度電子情報通信学会信越支部大会, pp. 128--128
2012 - レーシングカーの多目的最適設計のための並列型MOEA/D
今嶋 みのり; 佐藤 寛之
Oral presentation, Japanese, 進化計算学会,進化計算シンポジウム2012, pp. 6--13
2012 - MOEAにおける解の支配領域の適応的制御法とパレート形状に対する頑健性
冨田 浩平; 宮川 みなみ; 佐藤 寛之
Oral presentation, Japanese, 進化計算学会,進化計算シンポジウム2012, pp. 416--423
2012 - 集約関数の探索履歴を用いた交叉法による進化型多数目的最適化
堀野 将晴; 佐藤 寛之
Oral presentation, Japanese, 進化計算学会,進化計算シンポジウム2012, pp. 424--430
2012 - 複数制約付き多目的最適化における指向性交配のペア選択に関する検討
宮川 みなみ; 佐藤 寛之
Oral presentation, Japanese, 進化計算学会,進化計算シンポジウム2012, pp. 431--438
2012 - 学習分類子システムにおける最適行動獲得のための個体選択法
中田 雅也; Pier Luca Lanzi; 松島 裕康; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 進化計算学会,進化計算シンポジウム2012, pp. 304--311
2012 - m目的kナップザック問題における実行不可能解の修復法の検討
宮川 みなみ; 佐藤 寛之; 服部 聖彦; 髙玉 圭樹
Oral presentation, Japanese, 電子情報通信学会総合大会, pp. 73--73
2011 - Effects of Controlling the Number of Crossed Genes in S-CDAS Solving Many-Objective Optimization Problems
Hiroyuki Sato; Hernán Aguirre; Kiyoshi Tanaka
Oral presentation, English, 2011 Conv. Record of the Shin-etsu Chapter of IEICE, IEEE Session, pp. 185--185
2011 - 集約関数を用いる多目的EAの性能向上に関する一検討
堀野 将晴; 佐藤 寛之; 服部 聖彦; 高玉 圭樹
Oral presentation, Japanese, 電子情報通信学会総合大会,2011年度電子情報通信学会総合大会, pp. 123--123
2011 - 交叉する遺伝子量の制御法と突然変異の進化型多数目的最適化における解探索効果
佐藤 寛之; エルナン・アギレ; 田中 清
Oral presentation, Japanese, 平成23年度電子情報通信学会信越支部大会,2011年度電子情報通信学会総合大会, pp. 220--220
2011 - 進化による多数目的最適化における交叉する遺伝子量の適応的制御に関する一検討
佐藤 寛之; カルロス・コエロ; エルナン・アギレ; 田中 清
Oral presentation, Japanese, 平成23年度電子情報通信学会信越支部大会, pp. 162--162
2011 - 共通評価数に基づく重み最適化による映画推薦システムの精度向上に関する検討
坪井 利純; 佐藤 寛之; 兼子 正勝
Oral presentation, Japanese, 平成23年度電子情報通信学会信越支部大会, pp. 158--158
2011 - 解の支配領域制御法を用いたMOEAによる制約付き多目的最適化問題の解法に関する一検討
宮川 みなみ; 佐藤 寛之; 服部 聖彦; 高玉 圭樹
Oral presentation, Japanese, 平成23年度電子情報通信学会信越支部大会, pp. 154--154
2011 - ニュースサイト挿入型広告の相反する3目的に着目した最適デザイン
村松 憲征; 佐藤 寛之; 高玉 圭樹; 坂本 真樹
Oral presentation, Japanese, 日本認知科学会,第28回大会, pp. 428--434
2011 - 不確定環境下におけるマルチエージェント意志決定と協調行動
大谷 雅之; 崔 暁巍; 服部 聖彦; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 人工知能学会,JAWS2011 (Joint Agent Workshops and Symposium)
2011 - 多目的設計問題におけるパレート解理解支援システム
沢田石 祐弥; 牛田 裕也; 大谷 雅之; 服部 聖彦; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 人工知能学会,JAWS2011 (Joint Agent Workshops and Symposium)
2011 - 別カテゴリ商品提示による好みの明確化を促す推薦システムの設計と評価
佐藤 史盟; 大谷 雅之; 服部 聖彦; 佐藤 寛之; 高玉 圭樹; 山口 智浩
Oral presentation, Japanese, 計測自動制御学会,システム・情報部門 学術講演会 2011 (SSI2011), pp. 501--506
2011 - Tierra型オンボードコンピュータにおけるマルチビットアップセットへの耐性
原田 智広; 大谷 雅之; 市川 嘉裕; 服部 聖彦; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 日本航空宇宙学会,第55回宇宙科学技術連合講演会, 1A01(JSASS-2011-4016)
2011 - 進化型多数目的最適化における交叉する遺伝子量の適応的制御に関する検討
佐藤 寛之; カルロス・コエロ; エルナン・アギレ; 田中 清
Oral presentation, Japanese, 進化計算研究会,進化計算シンポジウム2011, pp. 36--43
2011 - A Many-Objective Evolutionary Algorithm Self-Controlling Dominance Area of Solutions
Hiroyuki Sato; Hernán Aguirre; Kiyoshi Tanaka
Oral presentation, English, 2010 Conv. Record of the Shinetsu Chapter of IEICE, IEEE Session, pp. 201--201
2010 - 解の支配領域を自己制御可能なCDAS法に関する一検討
佐藤 寛之; エルナン・アギレ; 田中 清
Oral presentation, Japanese, 人工知能学会,第４回進化計算フロンティア研究会, pp. 125--132
2010 - 部分支配を用いるMOEAにおけるアーカイブ戦略と効果
佐藤寛之; エルナン・アギレ; 田中 清
Oral presentation, Japanese, 電子情報通信学会,平成22年度電子情報通信学会信越支部大会, pp. 125--125
2010 - MOEAによる多数目的最適化における遺伝子情報の解析と操作に関する検討
佐藤寛之; エルナン・アギレ; 田中 清
Oral presentation, Japanese, 電子情報通信学会,平成22年度電子情報通信学会信越支部大会, pp. 126--126
2010 - 映画推薦システムにおけるGAを用いたユーザグループ最適化に関する一検討
坪井 利純; 佐藤寛之; 兼子 正勝
Oral presentation, Japanese, 電子情報通信学会,平成22年度電子情報通信学会信越支部大会, pp. 124--124
2010 - 多数目的最適化における変数空間の解析と遺伝的操作の検討
佐藤寛之; エルナン・アギレ; 田中 清
Oral presentation, Japanese, 進化計算研究会 進化計算シンポジウム2010,平成22年度電子情報通信学会信越支部大会, pp. 94--101
2010 - セレンディピティに基づく推薦システム
佐藤 史盟; 大瀧 篤; 服部 聖彦; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 人工知能学会,第24回全国大会, 3C3-3
2010 - 相反する目的を満たすニュースサイト広告のレイアウト最適化
村岡 和彦; 坂本 真樹; 高玉 圭樹; 佐藤 寛之
Oral presentation, Japanese, 計測自動制御学会,第37回知能システムシンポジウム, pp. 269--274
2010 - 個別化による学習分類子システムのマルチステップへの展開
中田 雅也; 市川 嘉裕; 松島 裕康; 佐藤 圭二; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 進化計算学会,進化計算シンポジウム 2010, pp. 295--302
2010 - 部分支配を用いる多目的進化型アルゴリズムの検討（その２）－多数目的最適化問題における効果の検証－
佐藤 寛之; エルナン・アギレ; 田中 清
Oral presentation, Japanese, 平成21年度電子情報通信学会信越支部大会, pp. 148--148
2009 - 部分支配を用いる多目的進化型アルゴリズムの検討（その１）－部分支配によるフロント分布の推移の調査－
佐藤 寛之; エルナン・アギレ; 田中 清
Oral presentation, Japanese, 平成21年度電子情報通信学会信越支部大会, pp. 147--147
2009 - 多目的進化型アルゴリズムにおける支配の拡張と効果
佐藤寛之; エルナン・アギレ; 田中 清
Oral presentation, Japanese, 人工知能学会,第1回進化計算フロンティア研究会, pp. 32--33
2009 - パレート部分支配を用いるMOEAの多数目的最適化における効果の検討
佐藤寛之; エルナン・アギレ; 田中 清
Oral presentation, Japanese, 進化計算研究会,進化計算シンポジウム2009, pp. 31--38
2009 - ハイパーボリュームの計算コスト削減に関する一検討
古渡 直哉; 佐藤 寛之; エルナン・アギレ; 田中 清
Oral presentation, Japanese, 進化計算研究会,進化計算シンポジウム2009, pp. 219--224
2009 - 進化型アルゴリズムによる指向性多目的最適化：IBEAの拡張
島田 智大; 松島 裕康; 佐藤 寛之; 服部 聖彦; 高玉 圭樹
Oral presentation, Japanese, 進化計算研究会,進化計算シンポジウム2009, pp. 131--138
2009 - Tierra型非同期GA：プログラム進化と維持
原田 智広; 大谷 雅之; 松島 裕康; 服部 聖彦; 佐藤 寛之; 高玉 圭樹
Oral presentation, Japanese, 進化計算研究会,進化計算シンポジウム2009, pp. 139--146
2009 - 支配を用いるMOEAの探索性能改善の一検討
土田今日子; 佐藤 寛之; エルナン・アギレ; 田中 清
Oral presentation, Japanese, 電子情報通信学会,平成20年度電子情報通信学会信越支部大会, p. 2
2008 - 支配を用いるMOEAの進化過程における機能変化
土田今日子; 佐藤 寛之; エルナン・アギレ; 田中 清
Oral presentation, Japanese, 電子情報通信学会,平成20年度電子情報通信学会信越支部大会, p. 1
2008 - CDAS法を組み込んだNSGA-IIとSPEA2の比較検討
佐藤寛之; エルナン・アギレ; 田中 清
Oral presentation, Japanese, 電子情報通信学会,平成20年度電子情報通信学会信越支部大会, p. 3
2008 - Hypervolumeを用いた2つのMOEAの相対的評価法
土田 今日子; 佐藤 寛之; エルナン・アギレ; 田中 清
Oral presentation, Japanese, 進化計算研究会,進化計算シンポジウム2008
2008 - CDAS法を適用したNSGA-IIとSPEA2の多数目的最適化における性能比較
佐藤寛之; エルナン・アギレ; 田中 清
Oral presentation, Japanese, 進化計算研究会,進化計算シンポジウム2008
2008 - 多目的進化型アルゴリズムを用いた多数目的最適化における支配の強弱制御の効果
佐藤寛之; エルナン・アギレ; 田中 清
Oral presentation, Japanese, 日本知能情報ファジィ学会,第17回インテリジェント・システム・シンポジウム(FAN2007), pp. 315--320
2007 - 多目的進化型アルゴリズムにおける支配の動的制御の基礎検討
土田今日子; 佐藤 寛之; エルナン・アギレ; 田中 清
Oral presentation, Japanese, 電子情報通信学会,平成19年度電子情報通信学会信越支部大会, pp. 66--66
2007 - MOEAによる多数目的最適化における支配の強弱制御の効果
佐藤寛之; エルナン・アギレ; 田中 清
Oral presentation, Japanese, 電子情報通信学会,平成19年度電子情報通信学会信越支部大会, pp. 205--205
2007 - 進化型アルゴリズムによる多目的最適化法とその動向
佐藤寛之; エルナン・アギレ; 田中 清
Oral presentation, Japanese, 計測自動制御学会,計測自動制御学会 中部支部シンポジウム2007, pp. 29--32
2007 - MOEAにおける支配の強弱が与える影響(その１)－グローバル支配の場合の検討－
佐藤寛之; エルナン・アギレ; 田中 清
Oral presentation, Japanese, 電子情報通信学会,平成18年度電子情報通信学会信越支部大会, pp. 132--132
2006 - MOEAにおける支配の強弱が与える影響(その２)－ローカル支配の場合の検討－
佐藤寛之; エルナン・アギレ; 田中 清
Oral presentation, Japanese, 電子情報通信学会,平成18年度電子情報通信学会信越支部大会, pp. 133--133
2006 - 多目的EAにおける局所支配および局所交叉に関する検討
佐藤寛之; エルナン・アギレ; 田中 清
Oral presentation, Japanese, 電子情報通信学会,平成17年度電子情報通信学会信越支部大会, pp. 225--226
2005 - 局所支配を用いた多目的EAの性能向上に関する検討
佐藤寛之; エルナン・アギレ; 田中 清
Oral presentation, Japanese, 電子情報通信学会,平成16年度電子情報通信学会信越支部大会, pp. 391--392
2004 - GAによる多目的最適化に関する一検討
佐藤寛之; エルナン・アギレ; 田中 清
Oral presentation, Japanese, 電子情報通信学会,平成15年度電子情報通信学会信越支部大会, pp. 265--266
2003
Courses
- Introduction to Info-Powered Energy
Apr. 2024 - Present
Tokyo University of Foreign Studies - Graduate Technical English
The University of Electro-Communications - Advanced Experiments A
The University of Electro-Communications - Engineering Seminer for Human Communication
The University of Electro-Communications - Advanced Evolutionary Computation
The University of Electro-Communications - Exercise in Informatics II
The University of Electro-Communications - Engineering Laboratory
The University of Electro-Communications - 現代の情報技術
Hoshi University - Intermediate Industrial Administration
Tokyo University of Science - Media Science and Engineering Laboratory
The University of Electro-Communications - Fundamentals of Computer Programming Exercises
The University of Electro-Communications - Innovative Comprehensive Communications Design 2
The University of Electro-Communications - Innovative Comprehensive Communications Design 1
The University of Electro-Communications - Academic Literacy
The University of Electro-Communications - Topics in Informatics Ⅰ (Evolutionary Computation)
The University of Electro-Communications
Affiliated academic society
- Apr. 2011 - Present
IEEE (Institute of Electrical and Electronics Engineers) - Apr. 2011 - Present
ACM (Association for Computing Machinery) - May 2010 - Present
The Japanese Society for Evolutionary Computation - Apr. 2010 - Present
The Japanese Society for Artificial Intelligence - Apr. 2009 - Present
Information Processing Society of Japan
Research Themes
- Multi-objective Evolutionary Computation Using Solution Set Aggregation
Hiroyuki Sato; Keiki Takadama
Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research, The University of Electro-Communications, Grant-in-Aid for Scientific Research (B), まず，当初計画の[研究項目A]として，目的空間における解集合アグリゲーションによって，推定パレートフロントを得る方法を構築した．具体的には，限られた既知の良好な解集合の目的関数値ベクトルをもとに，目的関数空間の各方向におけるパレートフロントの位置を，クリギング法や放射状基底関数ネットワークを用いて推定する．凸型，凹型，分離型のパレートフロントを有するテスト最適化問題において，提案法が，パレートフロントを良好に推定できることを明らかにした．また，提案法は，目的関数空間において，パレートフロントが存在しない方向についても，最良の目的関数値を示せることを明らかにした．これは，パレートフロントが存在しない目的空間の領域の目的関数値を意思決定者に説明する手段として有益であると考えられる．
つぎに，当初計画の[研究項目B]より先行して，[研究項目C]変数空間における解集合アグリゲーションによって，推定パレートセットを得る方法を構築した．これは，[研究項目A]の検討の過程で，[研究項目A]と共通の仕組みを用いて[研究項目C]を実現できることがわかってきたためである．具体的には，限られた既知の良好な解集合の変数値ベクトルをもとに，目的関数空間の各方向に対応する変数空間における位置をクリギング法や放射状基底関数ネットワークを用いて推定する．テスト問題において，提案法は，ランダム性を含む進化計算による解の生成法より，目的関数値の良好な解を獲得できることを明らかにした．また，実問題においても，効果があることを明らかにした．, 22H03660
Apr. 2022 - Mar. 2025 - Detecting, predicting, and deterring fake news using information-sharing models
Hiroshi Yoshiura; Hiroyuki Sato
Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research, Kyoto Tachibana University, Grant-in-Aid for Scientific Research (C), 本研究の期間は2021～2023年度であり、フェイクニュース対策の技術的な手法を追求している。フェイクニュースの拡散予測、フェイクニュースの検知精度の向上、フェイクニュースを信じるユーザの説得と社会の分断の緩和を取り上げている。2021年度には、フェイクニュースの拡散モデルの構築および、モデルを用いた拡散予測と検知を検討した。 当該年度（2022年度）には、ツイッター上のニュースから実データを収集して評価データを作成し、検知システムの精度および処理効率を向上した。評価データは、フェイクニュースとリアルニュース各10件から成り、各ニュースの発信者および転送者、各ユーザによる各ニュースの発信／転送時刻、ユーザ間のフォローフォロワー関係、ユーザがサンプルニュース以外に発信／転送した全てのつぶやきとその時刻から構成される。検知精度の向上については、フォローフォロワー関係のない孤立ユーザが検知精度の低下原因になることを見出し、フォローフォロワー関係のあるユーザのみから検知することで解決した。また、検知プログラムにおけるユーザIDの管理方法を改善することで処理時間を1/5に短縮し、大規模評価の見通しを得た。検知プログラムを評価データに適用し、フェイクニュースおよびリアルニュースに関わったユーザのニュースに対する信頼度およびユーザ間の信頼度を定量的に推定した。さらに、フェイクニュースは文章だけでなく、画像を用いる場合が多いので、投稿画像からのフェイクニュース検知の基礎検討を行った。, 21K11883
Apr. 2021 - Mar. 2024 - Simultaneous Problem Set Optimization Using Evolutionary Computation
Sato Hiroyuki
Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research, The University of Electro-Communications, Grant-in-Aid for Scientific Research (C), We studied evolutionary computation methodology to optimize multiple objective functions simultaneously. For continuous problems with real value design variables and discrete problems with discrete design variables, binary 0/1 especially, we respectively proposed test optimization problems that could specify similarities among objective functions. For these problems, we proposed evolutionary optimization algorithms that estimate similarities among objective functions by solution distributions in the variable space and utilize them to enhance or suppress the cooperative search in crossover generating new solutions by recombining two existing solutions. Results showed that the proposed algorithms could estimate similarities among multiple objective functions and utilized them to improve the simultaneous optimization of multiple objective functions compared to conventional evolutionary algorithms., 19K12135
01 Apr. 2019 - 31 Mar. 2023 - Search Algorithm Mapping of Evolutionary Multi-objective Optimization and Its Utilization on Unknown Problems
Sato Hiroyuki
Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research, The University of Electro-Communications, Grant-in-Aid for Young Scientists (B), Principal investigator, For multiple optimization problems with different characteristics, this research developed a mapping method of these optimization problems into a two-dimensional space to visually represent relationship strengths among these optimization problems. Also, this research developed a mapping method of optimization algorithms based on evolutionary computation into a two-dimensional space to visually represent relationship strengths among these optimization algorithms. Furthermore, based on these maps and their dada, this research developed an estimation method of the position of an unknown optimization problem on the optimization problem map generated by known optimization problems., 17K12750
01 Apr. 2017 - 31 Mar. 2020 - Multi-level robust solution search for evolutionary multi-objective design optimization
Sato Hiroyuki
Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research, The University of Electro-Communications, Grant-in-Aid for Young Scientists (B), In engineering design optimization, robust solutions (designs) with a small influence of noises are often employed even if their evaluation values are slightly worse than other candidate solutions. For noisy multi-objective optimization problems, this work designed an evolutionary optimization framework which the decision maker can consider not only evaluation values of each solution but its noise levels to select the final solution from the obtained solution set. First, this work proposed a method to approximate the optimal trade-off among multiple objectives and their noise levels. Next, since the number of noises should be minimized is increased with increasing the number of objectives, this work also proposed evolutionary many-objective optimization techniques. Furthermore, a decision making support user interface to effectively show high-dimensional vectors involving multiple objective values and their noises was proposed., 26730129
01 Apr. 2014 - 31 Mar. 2016 - 研究助成: 進化計算による大規模な工学設計最適化に関する研究
Hiroyuki Sato
大川情報通信基金, Principal investigator
2014 - 2014 - Development of effective evolutionary algorithms for many-objective optimization
SATO Hiroyuki
Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research, The University of Electro-Communications, Grant-in-Aid for Research Activity Start-up, To develop effective multi-objective evolutionary algorithms (MOEAs) for many-objective optimization problems which optimize more than four objective functions simultaneously, in this work we have developed a novel MOEA partially applying Pareto dominance and a method to self-control the dominance area of solutions. Also, to encourage the solutions search in many-objectives problems, we have developed a method of crossover controlling the number of crossed genes. The result of performance verification using benchmark problems revealed that proposed methods significantly improve the search performance of MOEA on many-objective optimization problems., 21800021
2009 - 2010
Industrial Property Rights
- 設備制御装置、設備制御方法および設備制御プログラム
Patent right, 太田 恵大, 橋本 昌典, 川崎 仁, 大谷 晋一郎, 村上 莉沙, 金子 洋介, 佐藤 寛之, 福原 洸平, 熊谷 涼, JP2021033246, Date applied: 10 Sep. 2021, 三菱電機株式会社, 国立大学法人電気通信大学, 特許第7408019号, Date registered: 21 Dec. 2023 - 情報処理装置、情報処理方法、プログラム
Patent right, 三井 康行, 佐藤 寛之, 山越 悠貴, 特願2022-078124, Date applied: 11 May 2022, アスクル株式会社, 特開2023-167161, Date announced: 24 Nov. 2023
Media Coverage
- 現実世界は最適化問題であふれている、SDGs時代にみる「進化計算」の可能性
IT media MONOist, Others, https://monoist.itmedia.co.jp/mn/articles/2210/27/news012.html
Oct. 2022 - アスクル、EC事業における“個口別れ”の解消に「進化計算」を適用
インプレス デジタルクロス, Others, https://dcross.impress.co.jp/docs/usecase/003208.html
Jun. 2022 - アスクルの物流センター「在庫配置最適化」に見るAI活用術 進化計算をコア技術に数100時間の処理を数時間に短縮 ＝アスクル、電気通信大学、タイムインターメディアが協業で実証実験を展開＝
日本文書情報マネジメント協会機関誌IM(Journal of Image &Information Management, JIIMA), Others
Apr. 2022 - ZEB (net Zero Energy Building)実現を，舞台裏で支えた技術を探る。
日経トレンディ2022年3月号, Others, https://www.mitsubishielectric.co.jp/corporate/special/hello-ai/pdf/202203_maisart_nikkei.pdf
Feb. 2022 - アスクル・電通大・タイムインターメディア、AIによる物流センター在庫配置最適化に向け協働で実証実験を開始
日本経済新聞, Paper, https://www.nikkei.com/article/DGXLRSP622824_R01C21A2000000/
Dec. 2021
Academic Contribution Activities
- Special Session on Make It Easy! Evolutionary Computation with Additional Objective Functions
Academic society etc, Planning etc, Shoichiro Tanaka, Keiki Takadama, Hiroyuki Sato, 30 Jun. 2024 - 05 Jul. 2024, 2024 IEEE World Congress on Computational Intelligence (IEEE WCCI 2024) - Special Session on Evolutionary Computation and Swarm Intelligence for Dynamical Environments and Multitasking Problems: Let Two Different Approaches Meet
Academic society etc, Planning etc, Keiki Takadama, Shio Kawakami, Hiroyuki Sato, 30 Jun. 2024 - 05 Jul. 2024, 2024 IEEE World Congress on Computational Intelligence (IEEE WCCI 2024) - Workshop on Computational Intelligence Applications
Academic society etc, Planning etc, Hiroyuki Sato, Akira Oyama, Ohta Yoshihiro, Takehisa Kohira, Takakuni Minewaki, Masaya Nakata, 30 Jun. 2024 - 05 Jul. 2024, IEEE World Congress on Computational Intelligence (IEEE WCCI 2024) - 企画セッション 進化計算の新展開
Academic society etc, Planning etc, 高木智章, 田中彰一郎, 二村成彦, 川上紫央, 大山聖, 佐藤寛之, 25 Nov. 2022 - 27 Nov. 2022, 計測自動制御学会 システム・情報部門 学術講演会 SSI2022 - パネルセッション 進化計算による実世界システムの最適化
Academic society etc, Planning etc, 佐藤 寛之,玉置 久,藤井 信忠,村田 忠彦, 25 Nov. 2022 - 27 Nov. 2022, AI時代のシステム学 －現実とインテリジェンスとシステム学, 計測自動制御学会 システム・情報部門 学術講演会 2022 (SSI2022) - Tutorial on Evolutionary Many-Objective Optimization
Academic society etc, Planning etc, Hisao Ishibuchi, Hiroyuki Sato, 18 Jul. 2022 - 23 Jul. 2022, 2022 IEEE Congress on Evolutionary Computation (IEEE CEC 2022) - Tutorial on Evolutionary Many-Objective Optimization
Academic society etc, Planning etc, Hisao Ishibuchi, Hiroyuki Sato, 28 Jun. 2021 - 01 Jul. 2021, 2021 IEEE Congress on Evolutionary Computation (IEEE CEC 2021) - Tutorial on Evolutionary Many-Objective Optimization
Academic society etc, Planning etc, Hisao Ishibuchi, Hiroyuki Sato, 19 Jul. 2020 - 24 Jul. 2020, 2020 IEEE Congress on Evolutionary Computation (IEEE CEC 2020) - Tutorial on Evolutionary Many-Objective Optimization
Academic society etc, Planning etc, Hisao Ishibuchi, Hiroyuki Sato, 08 Jul. 2020 - 12 Jul. 2020, 2020 The Genetic and Evolutionary Computation Conference (GECCO 2020) - Tutorial on Evolutionary Many-Objective Optimization
Academic society etc, Planning etc, Hisao Ishibuchi, Hiroyuki Sato, 13 Jul. 2019 - 17 Jul. 2019, 2019 The Genetic and Evolutionary Computation Conference (GECCO 2019) - Tutorial on Evolutionary Many-Objective Optimization
Academic society etc, Planning etc, Hisao Ishibuchi, Hiroyuki Sato, 10 Jun. 2019 - 13 Jun. 2019, 2019 IEEE Congress on Evolutionary Computation (IEEE CEC 2019) - Tutorial on Evolutionary Many-Objective Optimization
Academic society etc, Planning etc, Hisao Ishibuchi, Hiroyuki Sato, 08 Jul. 2018 - 13 Jul. 2018, 2018 IEEE World Congress on Computational Intelligence (IEEE WCCI 2018) - Special Session on Parallel and Distributed Evolutionary Computation in the Inter-Cloud Era (PDEC)
Academic society etc, Planning etc, Yuji Sato, Noriyuki Fujimoto, Hiroyuki Sato, 08 Jul. 2018 - 13 Jul. 2018, 2018 IEEE World Congress on Computational Intelligence (IEEE WCCI 2018) - 企画セッション 進化計算の新世代
Academic society etc, Planning etc, 佐藤 寛之, 宮川 みなみ, 25 Nov. 2017 - 27 Nov. 2017, 計測自動制御学会 システム・情報部門 学術講演会 SSI2017 - 企画セッション 進化計算の新世代
Academic society etc, Planning etc, 小野景子, 宮川みなみ, 佐藤寛之, 06 Dec. 2016 - 08 Dec. 2016, 計測自動制御学会 システム・情報部門 学術講演会 SSI2016 - Special Session on Many-objective Optimization
Academic society etc, Planning etc, Hiroyuki Sato, 24 Jul. 2016 - 29 Jul. 2016, 2016 IEEE World Congress on Computational Intelligence (IEEE WCCI 2016) - Special Session on Many-objective Optimization
Academic society etc, Planning etc, Hiroyuki Sato, 25 May 2015 - 28 May 2015, 2015 IEEE Congress on Evolutionary Computation (CEC 2015) - Workshop on Evolutionary Multi-Objective Optimization
Academic society etc, Planning etc, Hiroyuki Sato, 25 May 2015 - 28 May 2015, 2015 IEEE International Congress on Evolutionary Computation (CEC 2015) - Panel session on Evolutionary Many-Objective Optimization
Academic society etc, Planning etc, Hiroyuki Sato, Tomohiro Yoshikawa, Yusuke Nojima, 01 Jul. 2013 - 04 Jul. 2013, Panel Session of Recent Trends and Future Research Directions in Evolutionary Multiobjective Optimization in 17th Int'l Conf. on Intelligent System Applications to Power Systems (ISAP2013) - Special Session on Evolutionary Multiobjective Optimization and Multiple Criteria Decision Making
Academic society etc, Planning etc, Shinya Watanabe, Hiroyuki Sato, 20 Nov. 2012 - 24 Nov. 2012, The 6th International Conference on Soft Computing and Intelligent Systems and the 13th International Symposium on Advanced Intelligent Systems (SCIS&ISIS 2012)