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 Director
Degree
Commentator Guidehttps://www.kindai.ac.jp/meikan/456-handa-hisashi.html
URL
Mail
Last Updated :2020/09/30

Education and Career

Academic & Professional Experience

  •   2019 04 ,  - 現在, Professor, Faculty of Science and Engineering Department of Informatics, Kindai University
  •   2012 04 ,  - 2019 03 , Faculty of Science and Engineering, Department of Informatics, Kindai University
  •   2007 04 ,  - 2011 03 , Okayama University
  •   1998 04 ,  - 2007 03 , Faculty of Engineering, 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

Published Papers

  • Application of Evolutionary Multiobjective Optimization in L1-Regularization of CNN, Misaki Kitahashi, Hisashi Handa, 2129 - 2135, 2019
  • Solving Order/Degree Problems by Using EDA-GK with a Novel Sampling Method, Ryoichi Hasegawa, Hisashi Handa, 22(2), 236 - 241, 2018
  • Estimating Classroom Situations by Using CNN with Environmental Sound Spectrograms, Misaki Kitahashi, Hisashi Handa, Journal of Advanced Computational Intelligence and Intelligent Informatics, Journal of Advanced Computational Intelligence and Intelligent Informatics, 22(2), 242 - 248, 2018
  • Computer-Aided Diagnosis Based on Convolutional Neural Network System for Colorectal Polyp Classification: Preliminary Experience, Yoriaki Komeda, Hisashi Handa, Tomohiro Watanabe, Takanobu Nomura, Misaki Kitahashi, Toshiharu Sakurai, Ayana Okamoto, Tomohiro Minami, Masashi Kono, Tadaaki Arizumi, Mamoru Takenaka, Satoru Hagiwara, Shigenaga Matsui, Naoshi Nishida, Hiroshi Kashida, Masatoshi Kudo, Oncology, Oncology, 93(1), 30 - 34, 2017
  • Estimation of Distribution Algorithms with Graph Kernels for Graphs with Node Types, Kenta Maezawa, Hisashi Handa, Proc. 20th Asia Pacific Symposium on Intelligent and Evolutionary Systems, Proc. 20th Asia Pacific Symposium on Intelligent and Evolutionary Systems, 251 - 261, 2016
  • Use of RBM for Identifying Linkage Structures of Genetic Algorithms, Hisashi Handa, Proc. Eighth International Conference on Future Computational Technologies and Applications, Rome, Italy, Proc. Eighth International Conference on Future Computational Technologies and Applications, Rome, Italy, 41 - 46, 2016
  • Efficent Evolution of the Topology of Networks by the Estimation of Distribution Algorithms with Graph Kernels, Hisashi Handa, IEEE International Conference on Computational Intelligence and Communication Networks, Kolkata, India, IEEE International Conference on Computational Intelligence and Communication Networks, Kolkata, India, 2014
  • Memetic Algorithms of Graph-Based Estimation of Distribution Algorithms, Kenta Maezawa, Hisashi Handa, Proc. 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems, Proc. 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems, 647 - 656, 2014
  • Deep Boltzmann Machine for Evolutionary Agents of Mario AI, Handa Hisashi, 2014 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2014 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 36 - 41, 2014
    Summary:Deep Learning has attracted much attention recently since it can extract features taking account into the high-order knowledge. In this paper, we examine the Deep Boltzmann Machines for scene information of the Mario AI Championship. That is, the proposed method is composed of two parts: the DBM and a recurrent neural network. The DBM extracts features behind perceptual scene information, and it learns off-line. On the other hand, the recurrent neural network utilizes features to decide actions of the Mario AI agents, and it learns on-line by using Particle Swarm Optimization. Experimental results show the effectiveness of the proposed method.
  • Utilization of Soft Computing Techniques for Making COMMONS GAME Much More Exciting, Norio Baba, Yuta Arase, Masaki Takeda, Hisashi Handa, ADVANCES IN KNOWLEDGE-BASED AND INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, ADVANCES IN KNOWLEDGE-BASED AND INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, 243, 1991 - 1998, 2012 , Refereed
    Summary:Recently, we suggested that soft computing techniques such as NNs, MOEA-II, and FEP could be utilized for making educational games much more exciting. In this paper, we present game playing results of the original COMMONS GAME and two kinds of the modified COMMONS GAME having been carried out in several universities in Kansai area in Japan. These gaming results suggest that the modified COMMONS GAMES utilizing soft computing techniques such as NNs, MOEA-II, and FEP is by far the best in order to be utilized for letting people consider seriously about the "COMMONS" issue which is quite important not only for human beings but also all of the creatures in this planet.
  • Utilization of Evolutionary Algorithms for Making COMMONS GAME Much More Exciting, Norio Baba, Hisashi Handa, Mariko Kusaka, Masaki Takeda, Yuriko Yoshihara, Keisuke Kogawa, KNOWLEDGE-BASED AND INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT III, KNOWLEDGE-BASED AND INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT III, 6278, 555 - +, 2010 , Refereed
    Summary:In this paper, we suggest that soft computing techniques such as NNs, MOEA-II, and FEP could be utilized for making the original COMMONS GAME much more exciting. Several game playing results by our students confirm the effectiveness of our approach.
  • Dimension Reduction by Manifold Learning for Evolutionary Learning with Redundant Sensory Inputs, Hisashi Handa, Hiroshi Kawakami, 2010 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2010 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2010 , Refereed
    Summary:The optimization of the number and the alignment of sensors is quite important task for designing intelligent agents/robotics. Even though we could use excellent learning algorithms, it will not work well if the alignment of sensors is wrong or the number of sensors is not enough. In addition, if a large number of sensors are available, it will cause the delay of learning. In this paper, we propose the use of Manifold Learning for Evolutionary Learning with redundant sensory inputs in order to avoid the difficulty of designing the allocation of sensors. The proposed method is composed of two stages: The first stage is to generate a mapping from higher dimensional sensory inputs to lower dimensional space, by using Manifold Learning. The second stage is using Evolutionary Learning to learn control scheme. The input data for Evolutionary Learning is generated by translating sensory inputs into lower dimensional data by using the mapping.
  • Utilization of soft computing techniques for making environmental games more exciting-toward an effective utilization of the COMMONS GAME, Norio Baba, Kenta Nagasawa, Hisashi Handa, KNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT 2, PROCEEDINGS, KNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT 2, PROCEEDINGS, 5178, 411 - +, 2008 , Refereed
    Summary:We have suggested that utilization of soft computing techniques such as GAs and EAs could contribute a lot for making the original COMMONS GAME much more exciting. In this paper, we try to find an answer concerning to the question "Which game is the best for letting people consider seriously about the commons among the three games (the original COMMONS GAME, the modified COMMONS GAME utilizing GAs & NNs, and the modified COMMONS GAME utilizing EAs & NNs) ?". Several game playing by our Students confirm that the modified COMMONS GAME utilizing EAs & NNs can provide the best chance for letting players consider seriously about the commons.
  • COMMONS GAME made more exciting by an intelligent utilization of the two evolutionary algorithms, Norio Baba, Hisashi Handa, Studies in Computational Intelligence, Studies in Computational Intelligence, 71, 1 - 16, 2007 , Refereed
    Summary:In this paper, we suggest that Evolutionary Algorithms could be utilized in order to let the COMMONS GAME, one of the most popular environmental games, become much more exciting. In order to attain this objective, we utilize Multi-Objective Evolutionary Algorithms to generate various skilled players. Further, we suggest that Evolutionary Programming could be utilized to find out an appropriate point of each card at the COMMONS GAME. Several game playings utilizing the new rule of the COMMONS GAME confirm the effectiveness of our approach. © 2007 Springer-Verlag Berlin Heidelberg.
  • Evolutionary computations for designing game rules of the COMMONS GAME, Hisashi Handa, Norio Baba, 2007 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND GAMES, 2007 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND GAMES, 334 - 339, 2007 , Refereed
    Summary:In this paper, we focus on game rule design by using two Evolutionary Computations. The first EC is a Multi-Objective Evolutionary Algorithm in order to generate various skilled players. By using acquired skilled players, i.e., Pareto individuals in MOEA, another EC (Evolutionary Programming) adjusts game rule parameters he, an appropriate point of each card in the COMMONS GAME.
  • Utilization of evolutionary algorithms for making the environmental game much more exciting, Norio Baba, Hisashi Handa, 2007 INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS, VOLS 1-6, 2007 INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS, VOLS 1-6, 876 - 879, 2007 , Refereed
    Summary:In this paper, we suggest that EAs could be utilized for making the Environmental Game become much more exciting. In particular, in order attain this objective, we try to utilize Evolutionary Algorithms in the following steps: (1) First, we consider a new way for producing a return for each player. (2) Second, we utilize Multi-Objective Evolutionary Algorithms (MOEAs) to generate various skilled players whose decision making concerning the investment to the chemical goods (and consideration toward environmental protection) can strongly affect the environmental state of the sea. (3) Further, we utilize Evolutionary Programming (EP) to derive appropriate rules which could be used to help players fully enjoy game playing. Several game playing (utilizing the new rule) by our students suggest the effectiveness of our approach.
  • A study of information flow between designers and users via website focused on property of hyper links, Hidetsugu Suto, Hiroshi Kawakami, Hisashi Handa, HUMAN INTERFACE AND THE MANAGEMENT OF INFORMATION: METHODS, TECHNIQUES AND TOOLS IN INFORMATION DESIGN, PT 1, PROCEEDINGS, HUMAN INTERFACE AND THE MANAGEMENT OF INFORMATION: METHODS, TECHNIQUES AND TOOLS IN INFORMATION DESIGN, PT 1, PROCEEDINGS, 4557, 189 - 198, 2007 , Refereed
    Summary:The accessibility of information on web-space is considered to be focused on the properties of hyperlinks. Although the reasons to put in a hyperlink are multifarious, only one kind of link exists in HTML structure. As a result of this, users have to search for links by using syntactically techniques so as to retrieve information in web-space. In this paper, four kinds of hyper links are introduced into HTML, in order to improve the accessibility of information in web-space: External links, Internal links, Navigation links and Intra-unit links. Designers and users can share the image of the link target page by using these kinds of links.
  • Coevolutionary GA with schema extraction by machine learning techniques and its application to knapsack problems, H Handa, T Horiuchi, O Katai, T Kaneko, T Konishi, M Baba, PROCEEDINGS OF THE 2001 CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1 AND 2, PROCEEDINGS OF THE 2001 CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1 AND 2, 1213 - 1219, 2001 , Refereed
    Summary:In this paper, we will introduce a new Coevolutionary Genetic Algorithm with schema extraction by machine learning techniques. Our CGA consists of two GA populations: the first GA (H-GA) searches for the solutions in the given problems and the second GA (P-GA) searches for effective schemata of the H-GA. We aim to improve the search ability of our CGA by extracting more efficiently useful schemata from the H-GA population, and then incorporating those extracted schemata in natural manner into the P-GA. Several computational simulations on multidimensional knapsack problems confirm us the effectiveness of the proposed method.
  • Evolving neural networks with pruning operator for constructing game playing agents, H Handa, T Horiuchi, T Konishi, M Baba, KNOWLEDGE-BASED INTELLIGENT INFORMATION ENGINEERING SYSTEMS & ALLIED TECHNOLOGIES, PTS 1 AND 2, KNOWLEDGE-BASED INTELLIGENT INFORMATION ENGINEERING SYSTEMS & ALLIED TECHNOLOGIES, PTS 1 AND 2, 69, 910 - 913, 2001 , Refereed
    Summary:In game programming for Chess, Reversi, and so on, it is difficult to evaluate each state and to decide which attributes should be used for state evaluation. Hence, state evaluation functions are often designed by using heuristics of human. In this paper. we adopt a neural network as a state evaluation function for game player, and evolve its weights and structure by Evolutionary Strategy (ES). In the proposed method, we attempt to acquire better state evaluation function by evolving weights and structure simultaneously.
  • Coevolutionary searching method by double-layered population with genetic information exchange, Hisashi Handa, Osamu Katai, Norio Baba, Tetsuo Sawaragi, Australian Journal of Intelligent Information Processing Systems, Australian Journal of Intelligent Information Processing Systems, 4(3/4), 196 - 205, 1998 , Refereed

Books etc

  • Markov Networks in Evolutionary Computation, EDA-RL: EDA with Conditional Random Fields for Solving Reinforcement Learning Problems, Joint author, 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, Hiroshi Someya, Hisashi Handa, Seiichi Koakutsu, IEEJ Transactions on Electronics, Information and Systems, 132, 1, 2, 5,   2012 , 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. © 2012 The Institute of Electrical Engineers of Japan.
  • 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 algorithms for gesture segmentation, H. Handa, H. Kawakami, Proceedings of the SICE Annual Conference, 1722, 1723,   2010 11 , http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=78649237000&origin=inward
    Summary:From the viewpoint of "FUBEN-EKI," we have developed an inconvenient input method for mobile devices, which provides us with chances of exploration. In the proposed mobile device, users provide a large number of motions to systems, which shoud be learnt. In this paper, we constitute evolutionary algorithms to automatically extract frequently-observed motions from whole the motion sequence. © 2010 SICE.
  • Evolutionary technologies: Fundamentals and applications to information/communication systems and manufacturing/logistics systems, Mitsuo Gen, Hiroshi Kawakami, Yasuhiro Tsujimura, Hisashi Handa, Lin Lin, Azuma Okamoto, IEEJ Transactions on Electronics, Information and Systems, 130, 5, 731, 736,   2010 , 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. © 2010 The Institute of Electrical Engineers of Japan.
  • Estimation of distribution algorithms for solving reinforcement learning problems, Hisashi Handa, IEEJ Transactions on Electronics, Information and Systems, 130, 5, 4, 765,   2010 , 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. © 2010 The Institute of Electrical Engineers of Japan.
  • 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, Hisashi Handa, Hiroshi Kawakami, Osamu Katai, IEEJ Transactions on Electronics, Information and Systems, 128, 3, 334, 339,   2008 , 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. © 2008 The Institute of Electrical Engineers of Japan.
  • 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
  • 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.
  • Solving constraint satisfaction problems by memetic algorithms using estimation of distribution algorithms, Hisashi Handa, Transactions of the Japanese Society for Artificial Intelligence, 19, 5, 405, 412,   2004 , 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.
  • 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., 85, 6, 1025, 1037,   2002 06 01 , http://id.ndl.go.jp/bib/6176049
  • 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
  • User modeling in the case of artifact operation based on Channel Theory, Hisashi Handa, Hiroshi Kawakami, Hidetsugu Suto, Proceedings of the SICE Annual Conference, 2282, 2285,   2007 , 10.1109/SICE.2007.4421369, http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=50249132029&origin=inward
    Summary:User-modeling on operations is quite useful to analyze possible error caused by human. In this paper, we propose a new framework of user-modeling method in the case of artifact operation based on Channel Theory. In the proposed framework, two knowledge domains for interface and functions in artifacts are described as classifications at first. Moreover, the logical consistency between these knowledge domains are kept by using mathematical tools in the Channel Theory. Then, user-modeling can be reasoned from these knowledge domains. © 2007 SICE.
  • Solving Constraint Satisfaction Problems by using Coevolutionary Genetic Algorithms, H Handa, O Katai, N Baba, T Sawaragi, 1998 IEEE INTERNATIONAL CONFERENCE ON EVOLUTIONARY COMPUTATION - PROCEEDINGS, 21, 26,   1998
    Summary:In this paper, Coevolutionary Genetic Algorithm for solving Constraint Satisfaction Problems (CSPs) is proposed. It consists of two Genetic Algorithms (GAs): a traditional GA and another GA to search for good schemata in the former GA. These GAs evolve in two levels, i.e., phenotype-level and schema-level, and affect with each other through genetic operations. To search for solutions effectively, we devise new genetic operator by utilizing search mechanism of solution synthesis approach used in CSP community. Computational results on general CSPs confirm the effectiveness of our approach.
  • A Consideration of Coevolutionary GA with Real Coding, Handa, H, Katai, O, Sawaragi, T, Baba, N, SICE-ANNUAL CONFERENCE-, 711, 712,   1998
    Summary:SOCIETY OF INSTRUMENT AND CONTROL ENGINEERS (SICE)
  • Genetic algorithm involving coevolution mechanism to search for effective genetic information, H Handa, N Baba, O Katai, T Sawaragi, T Horiuchi, PROCEEDINGS OF 1997 IEEE INTERNATIONAL CONFERENCE ON EVOLUTIONARY COMPUTATION (ICEC '97), 709, 714,   1997 , Refereed
    Summary:A new genetic algorithm which exploits an idea of ''coevolution'' is proposed. The proposed method consists of two GAs: Host GA and Parasite GA. The Host GA searches for the solutions, and these two GAs are closely related to each other. The Parasite GA plays an important role in searching for useful schemata in the Host GA. Furthermore, two methods of fitness evaluation of Parasite GA are examined: differentiating method and averaging method. The differentiating method will yield the search for schemata that are not yet discovered by the Host GA. The averaging method will yield the search for schemata that have high average of fitness. Various computer simulations confirm the effectiveness of the proposed methods.