KINDAI UNIVERSITY


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HANDA Hisashi

Profile

FacultyDepartment of Informatics / Graduate School of Science and Engineering Research / Kindai University Cyber Informatics Research Institute
PositionProfessor
Degree
Commentator Guidehttps://www.kindai.ac.jp/meikan/456-handa-hisashi.html
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Last Updated :2020/04/03

Education and Career

Academic & Professional Experience

  •   2012 04 ,  - 現在, Faculty of Science and Engineering, Department of Informatics, Kindai University
  •   2007 ,  - 2011 , Okayama University

Research Activities

Research Areas

  • Informatics, Sensitivity (kansei) informatics
  • Informatics, Soft computing
  • Informatics, Intelligent robotics
  • Informatics, Perceptual information processing
  • Manufacturing technology (mechanical, electrical/electronic, chemical engineering), Measurement engineering
  • Manufacturing technology (mechanical, electrical/electronic, chemical engineering), Control and systems engineering
  • Manufacturing technology (mechanical, electrical/electronic, chemical engineering), Control and systems engineering
  • Informatics, Mechanics and mechatronics
  • Informatics, Robotics and intelligent systems

Research Interests

  • State Space Construction, Reinforcement Learning, Anticipatory Behavior, Autonomous Robots, Growing Neural Gas Neural Networks

Books etc

  • Markov Networks in Evolutionary Computation, EDA-RL: EDA with Conditional Random Fields for Solving Reinforcement Learning Problems, 共著, Springer Berlin Heidelberg,   2012 05

Misc

  • An Application to Voice Synthesizer as Phonetic Disorder Aid, KAWAKAMI Hiroshi, HANDA Hisashi, ABE Masanobu, 51, 8, 765, 770,   2012 08 10 , http://ci.nii.ac.jp/naid/10031007317
  • D-9-5 A lifelog collecting tool by smart phone, Fujioka Daisuke, Handa Hisashi, Abe Masanobu, Proceedings of the IEICE General Conference, 2012, 1,   2012 03 06 , http://ci.nii.ac.jp/naid/110009461310
  • D-14-12 Quality improvement by mixture of HMM-based speech synthesis and natural voice, INOUE Takuma, HANDA Hisashi, ABE Masanobu, Proceedings of the IEICE General Conference, 2012, 1,   2012 03 06 , http://ci.nii.ac.jp/naid/110009461369
  • Designing Stochastic Optimization Algorithms for Real-world Applications, SOMEYA Hiroshi, HANDA Hisashi, KOAKUTSU Seiichi, IEEJ Transactions on Electronics, Information and Systems, 132, 1, 2, 5,   2012 01 01 , 10.1541/ieejeiss.132.2, http://ci.nii.ac.jp/naid/10030528775
    Summary:This article presents a review of recent advances in stochastic optimization algorithms. Novel algorithms achieving highly adaptive and efficient searches, theoretical analyses to deepen our understanding of search behavior, successful implementation on parallel computers, attempts to build benchmark suites for industrial use, and techniques applied to real-world problems are included. A list of resources is provided.
  • Estimation of Distribution Algorithms for Function Optimization, HANDA Hisashi, Journal of Japan Society for Fuzzy Theory and Intelligent Informatics, 23, 1, 12, 17,   2011 02 15 , 10.3156/jsoft.23.1_12, http://ci.nii.ac.jp/naid/10028090047
  • Evolutionary Technologies : Fundamentals and Applications to Information/Communication Systems and Manufacturing/Logistics Systems, GEN Mitsuo, KAWAKAMI Hiroshi, TSUJIMURA Yasuhiro, HANDA Hisashi, LIN Lin, OKAMOTO Azuma, IEEJ Transactions on Electronics, Information and Systems, 130, 5, 731, 736,   2010 05 01 , 10.1541/ieejeiss.130.731, http://ci.nii.ac.jp/naid/10026228865
    Summary:As efficient utilization of computational resources is increasing, evolutionary technology based on the Genetic Algorithm (GA), Genetic Programming (GP), Evolution Strategy (ES) and other Evolutionary Computations (ECs) is making rapid progress, and its social recognition and the need as applied technology are increasing. This is explained by the facts that EC offers higher robustness for knowledge information processing systems, intelligent production and logistics systems, most advanced production scheduling and other various real-world problems compared to the approaches based on conventional theories, and EC ensures flexible applicability and usefulness for any unknown system environment even in a case where accurate mathematical modeling fails in the formulation. In this paper, we provide a comprehensive survey of the current state-of-the-art in the fundamentals and applications of evolutionary technologies.
  • Estimation of Distribution Algorithms for Solving Reinforcement Learning Problems, HANDA Hisashi, IEEJ Transactions on Electronics, Information and Systems, 130, 5, 758, 765,   2010 05 01 , 10.1541/ieejeiss.130.758, http://ci.nii.ac.jp/naid/10026228942
    Summary:Estimation of Distribution Algorithms (EDAs) are a promising evolutionary computation method. Due to the use of probabilistic models, EDAs can outperform conventional evolutionary computation. In this paper, EDAs are extended to solve reinforcement learning problems which are a framework for autonomous agents. In the reinforcement learning problems, we have to find out better policy of agents such that it yields a large amount of reward for the agents in the future. In general, such policy can be represented by conditional probabilities of agents' actions, given the perceptual inputs. In order to estimate such a conditional probability distribution, Conditional Random Fields (CRFs) by Lafferty (2001) are introduced into EDAs. The reason why CRFs are adopted is that CRFs are able to learn conditional probabilistic distributions from a large amount of input-output data, i.e., episodes in the case of reinforcement learning problems. Computer simulations on Probabilistic Transition Problems and Perceptual Aliasing Maze Problems show the effectiveness of EDA-RL.
  • Rule Acquisition for Cognitive Agents by Using Estimation of Distribution Algorithms, NISHIMURA Tokue, HANDA Hisashi, 2008, 6, 31, 36,   2008 07 23 , http://ci.nii.ac.jp/naid/10025652465
  • Recent Advances in Evolutionary Computation, HANDA Hisashi, KAWAKAMI Hiroshi, KATAI Osamu, IEEJ Transactions on Electronics, Information and Systems, 128, 3, 334, 339,   2008 03 01 , 10.1541/ieejeiss.128.334, http://ci.nii.ac.jp/naid/10021131467
    Summary:Recent developments in the field of Evolutionary Computation have led to a renewed interest in its real-world applications on wide variety of application domains. This paper attempts to provide a comprehensive overview of recent advances of Evolutionary Computation studies. First, we describe the basic mechanisms of Evolutionary Computation. Moreover, a devlopment diagram of Evolutionary Computations is also shown in order to understand research perspecitve in this field. Next, several active research fields, such as Evolutionary Optimization on Dynamic/Uncertain Environments, Evolutionary Multi-objective Optimization, Estimation of Distribution Algorithms, Swarm Intelligence, and Game Application, are introduced.
  • Evolutionary Algorithms based on Probabilistic Models : Recent Advances in Estimation of Distribution Algorithms, HANDA Hisashi, Systems, control and information, 51, 5, 224, 229,   2007 05 15 , 10.11509/isciesci.51.5_224, http://ci.nii.ac.jp/naid/110006272972
  • A Report on 2006 IEEE World Congress on Computational Intelligence (WCCI2006), HANDA Hisashi, 50, 12,   2006 12 15 , http://ci.nii.ac.jp/naid/10018580131
  • A Report on 2006 IEEE World Congress on Computational Intelligence (WCCI2006), HANDA Hisashi, Systems, control and information, 50, 12,   2006 12 15 , http://ci.nii.ac.jp/naid/110006160198
  • Solving Constraint Satisfaction Problems by Memetic Algorithms Using Estimation of Distribution Algorithms, HANDA Hisashi, Transactions of the Japanese Society for Artificial Intelligence, 19, 405, 412,   2004 11 01 , 10.1527/tjsai.19.405, http://ci.nii.ac.jp/naid/10014164752
    Summary:Estimation of Distribution Algorithms, which employ probabilistic models to generate the next population, are new promising methods in the field of genetic and evolutionary algorithms. In the case of conventional Genetic and Evolutionary Algorithms are applied to Constraint Satisfaction Problems, it is well-known that the incorporation of the domain knowledge in the Constraint Satisfaction Problems is quite effective. In this paper, we constitute a memetic algorithm as a combination of the Estimation of Distribution Algorithm and a repair method. Experimental results on general Constraint Satisfaction Problems tell us the effectiveness of the proposed method.
  • Reinforcement Learning with State Segmentation Method based on Sensory Change Anticipations, HANDA Hisashi, IEICE technical report. Neurocomputing, 104, 349, 75, 79,   2004 10 12 , http://ci.nii.ac.jp/naid/110003234067
    Summary:The situatedness is one of the most important notion for constructing state segmentation of the reinforcement learning agents. Hence, we propose a new state segmentation method referring to sensation-action series. The proposed method quantizes input space, and anticipates the next inputs as a consequence of actions of agents. Moreover, the proposed method constitutes the state segmentation of agents based on anticipation accuracy.
  • A New Rangefinder for Three-Dimensional Shape Measurement of Objects with Unknown Reflectance, BABA Mitsuru, OHTANI Kozo, IMAI Makoto, HANDA Hisashi, The transactions of the Institute of Electronics, Information and Communication Engineers. D-II, 85, 6, 1025, 1037,   2002 06 01 , http://ci.nii.ac.jp/naid/110003184265
  • An Incremental State-Space Construction Based on the Notion of Contradiction for Reinforcement Learningt, HANDA Hisashi, NINONIIYA Akira, HORIUCHI Tadashi, KONISHI Tadataka, BABA Mitsuru, 38, 5, 469, 476,   2002 05 31 , 10.9746/sicetr1965.38.469, http://ci.nii.ac.jp/naid/10008466469
  • Evolutionary Artifacts Design with a Generative Encoding Method, HANDA Hisashi, 29, 13, 16,   2002 03 28 , http://ci.nii.ac.jp/naid/10015644869
  • A Report of Attending to the 2001 Congress on Evolutionary Computation (CEC2001), HANDA Hisashi, Systems, control and information, 45, 9,   2001 09 15 , http://ci.nii.ac.jp/naid/110003891985
  • A Novel Hybrid Framework of Genetic Algorithm and Machine Learning, HORIUCHI Tadashi, HANDA Hisashi, KANEKO Takayuki, KATAI Osamu, 17, 439, 440,   2001 09 05 , http://ci.nii.ac.jp/naid/10015750135
  • Coevolutionary GA with a Stochastic Genetic Repair Operator for Solving Constraint Satisfaction Problems, HANDA Hisashi, KATAI Osamu, WATANABE Katsuyuki, KONISHI Tadataka, BABA Mitsuru, 37, 6, 567, 576,   2001 06 30 , 10.9746/sicetr1965.37.567, http://ci.nii.ac.jp/naid/10006210534
  • A Reinforcement Learning with an Incremental State-Segmentation Method Based upon Perception-Action Records, HANDA Hisashi, NINOMIYA Akira, HORIUCHI Tadashi, KONISHI Tadataka, 10, 113, 116,   2000 10 , http://ci.nii.ac.jp/naid/10016715493
  • A Proposal of Coevolutionary Fuzzy Classifier System, NODA Takashi, HANDA Hisashi, KONISHI Tadataka, KATAI Osamu, 16, 325, 326,   2000 09 06 , http://ci.nii.ac.jp/naid/10017211641
  • Shape Measurement of Columnar Objects with Specular Surfaces by Slit Ray Projectiton Method, BABA Mitsuru, KONISHI Tadataka, HANDA Hisashi, The transactions of the Institute of Electronics, Information and Communication Engineers. D-II, 83, 8, 1773, 1782,   2000 08 25 , http://ci.nii.ac.jp/naid/110003183906
  • Operations based on Occurrence Ordering of Structural Changes in the case of A Network Representation of Physical Systems, MOTOYOSHI Kengo, KONISHI Tadataka, KAWAKAMI Hiroshi, HANDA Hisashi, Proceedings of the Annual Conference of JSAI, 14, 435, 438,   2000 07 03 , http://ci.nii.ac.jp/naid/10009929400
  • An Incremental State-Segmentation Method for Reinforcement Learning Based on the Notion of Contradiction, NINOMIYA Akira, HANDA Hisashi, KONISHI Tadataka, 27, 199, 204,   2000 03 23 , http://ci.nii.ac.jp/naid/10016721511
  • Proposal of a New Genetic Algorithm Utilizing a Mechanism of Co-Evolution, HANDA Hisashi, KATAI Osamu, BABA Norio, SAWARAGI Tetsuo, KONISHI Tadataka, BABA Mitsuru, Trans. of the SICE, 35, 11, 1438, 1446,   1999 11 30 , 10.9746/sicetr1965.35.1438, http://ci.nii.ac.jp/naid/10004577377
  • An Estimation Method of Shape from Shading using GA, HANDA Hisashi, BABA Mitsuru, KONISHI Tadataka, KATAI Osamu, 9, 253, 258,   1999 10 , http://ci.nii.ac.jp/naid/10016716592
  • Solving Dynamic CSPs by Coevolutionary GA, HANDA Hisashi, KATAI Osamu, KONISHI Tadataka, BABA Mitsuru, 26, 63, 68,   1999 03 24 , http://ci.nii.ac.jp/naid/10016720837
  • A Consideration of the Coding and the Fitness Evaluation in the case of Coevolutionary GA, HANDA Hisashi, KATAI Osamu, SAWARAGI Tetsuo, BABA Norio, 25, 71, 74,   1998 03 19 , http://ci.nii.ac.jp/naid/10016720444
  • New Genetic Algorithm with Coevolutionary Search for Useful Schemata, HANDA Hisashi, BABA Norio, KATAI Osamu, SAWARAGI Tetsuo, HORIUCHI Tadashi, 24, 145, 150,   1997 03 18 , http://ci.nii.ac.jp/naid/10016720198