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Tadahiko Murata: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Hisao Ishibuchi, Tomoharu Nakashima, Tadahiko Murata
    Multiobjective Optimization in Linguistic Rule Extraction from Numerical Data. [Citation Graph (0, 0)][DBLP]
    EMO, 2001, pp:588-602 [Conf]
  2. Tadahiko Murata, Hisao Ishibuchi, Mitsuo Gen
    Specification of Genetic Search Directions in Cellular Multi-objective Genetic Algorithms. [Citation Graph (0, 0)][DBLP]
    EMO, 2001, pp:82-95 [Conf]
  3. Tadahiko Murata, Hiroyuki Nozawa, Hisao Ishibuchi, Mitsuo Gen
    Modification of Local Search Directions for Non-dominated Solutions in CellularMultiobjective Genetic Algorithms forPattern Classification Problems. [Citation Graph (0, 0)][DBLP]
    EMO, 2003, pp:593-607 [Conf]
  4. Tadahiko Murata, Ryota Itai
    Local Search in Two-Fold EMO Algorithm to Enhance Solution Similarity for Multi-objective Vehicle Routing Problems. [Citation Graph (0, 0)][DBLP]
    EMO, 2006, pp:201-215 [Conf]
  5. Tadahiko Murata, Hisao Ishibuchi, Tomoharu Nakashima, Mitsuo Gen
    Fuzzy Partition and Input Selection by Genetic Algorithms for Designing Fuzzy Rule-Based Classification Systems. [Citation Graph (0, 0)][DBLP]
    Evolutionary Programming, 1998, pp:407-416 [Conf]
  6. Hisao Ishibuchi, Tadashi Yoshida, Tadahiko Murata
    Balance Between Genetic Search And Local Search In Hybrid Evolutionary Multi-criterion Optimization Algorithms. [Citation Graph (0, 0)][DBLP]
    GECCO, 2002, pp:1301-1308 [Conf]
  7. Tadahiko Murata, Hisao Ishibuchi, Mitsuo Gen
    Cellular Genetic Local Search for Multi-Objective Optimization. [Citation Graph (0, 0)][DBLP]
    GECCO, 2000, pp:307-314 [Conf]
  8. Tadahiko Murata, Shiori Kaige, Hisao Ishibuchi
    Generalization of Dominance Relation-Based Replacement Rules for Memetic EMO Algorithms. [Citation Graph (0, 0)][DBLP]
    GECCO, 2003, pp:1234-1245 [Conf]
  9. Tadahiko Murata, Takashi Nakamura
    Multi-agent Cooperation Using Genetic Network Programming with Automatically Defined Groups. [Citation Graph (0, 0)][DBLP]
    GECCO (2), 2004, pp:712-714 [Conf]
  10. Tadahiko Murata, Takashi Nakamura
    Genetic network programming with automatically defined groups for assigning proper roles to multiple agents. [Citation Graph (0, 0)][DBLP]
    GECCO, 2005, pp:1705-1712 [Conf]
  11. Tadahiko Murata, Masatoshi Yamaguchi
    Neighboring crossover to improve GA-based Q-learning method for multi-legged robot control. [Citation Graph (0, 0)][DBLP]
    GECCO, 2005, pp:145-146 [Conf]
  12. Tadahiko Murata, Nobuaki Kakito
    Products' review page projection using the number of evaluating expressions. [Citation Graph (0, 0)][DBLP]
    ICWI, 2004, pp:1061-1064 [Conf]
  13. Tadahiko Murata, Shoko Shirato
    History using browsing duration to assist browsing activity. [Citation Graph (0, 0)][DBLP]
    ICWI, 2004, pp:1121-1124 [Conf]
  14. Hisao Ishibuchi, Tadahiko Murata
    Multi-Objective Genetic Local Search Algorithm. [Citation Graph (0, 0)][DBLP]
    International Conference on Evolutionary Computation, 1996, pp:119-124 [Conf]
  15. Hisao Ishibuchi, Tomoharu Nakashima, Tadahiko Murata
    Genetic-Algorithm-Based Approaches to the Design of Fuzzy Systems for Multi-Dimensional Pattern Classification Problems. [Citation Graph (0, 0)][DBLP]
    International Conference on Evolutionary Computation, 1996, pp:229-234 [Conf]
  16. Tadahiko Murata, Hisao Ishibuchi
    Performance Evaluation of Genetic Algorithms for Flowshop Scheduling Problems. [Citation Graph (0, 0)][DBLP]
    International Conference on Evolutionary Computation, 1994, pp:812-817 [Conf]
  17. Tadahiko Murata, Hisao Ishibuchi
    Positive and Negative Combination Effects of Crossover and Mutation Operators in Sequencing Problems. [Citation Graph (0, 0)][DBLP]
    International Conference on Evolutionary Computation, 1996, pp:170-175 [Conf]
  18. Hisao Ishibuchi, Tadahiko Murata, Shigemitsu Tomioka
    Effectiveness of Genetic Local Search Algorithms. [Citation Graph (0, 0)][DBLP]
    ICGA, 1997, pp:505-512 [Conf]
  19. Tadahiko Murata, Hiroshi Matsumoto
    Use of Successful Policies to Relearn for Induced States of Failure in Reinforcement Learning. [Citation Graph (0, 0)][DBLP]
    KES, 2004, pp:1114-1120 [Conf]
  20. Tadahiko Murata, Kenji Takada
    Performance Evaluation of a Distributed Genetic Algorithm with Cellular Structures on Function Optimization Problems. [Citation Graph (0, 0)][DBLP]
    KES, 2004, pp:1128-1135 [Conf]
  21. Hisao Ishibuchi, Tomoharu Nakashima, Tadahiko Murata
    A Fuzzy Classifier System That Generates Linguistic Rules for Pattern Classification Problems. [Citation Graph (0, 0)][DBLP]
    IEEE/Nagoya-University World Wisepersons Workshop, 1995, pp:35-54 [Conf]
  22. Tadahiko Murata, Yu Mizoguchi
    Generation and recall method for long-term memory data to suppress interference in RAN. [Citation Graph (0, 0)][DBLP]
    SMC (6), 2004, pp:5697-5701 [Conf]
  23. Hisao Ishibuchi, Tomoharu Nakashima, Tadahiko Murata
    Three-objective genetics-based machine learning for linguistic rule extraction. [Citation Graph (0, 0)][DBLP]
    Inf. Sci., 2001, v:136, n:1-4, pp:109-133 [Journal]
  24. Hisao Ishibuchi, Tadahiko Murata, Tomoharu Nakashima
    Linguistic Rule Extraction from Numerical Data for High-dimensional Classification Problems. [Citation Graph (0, 0)][DBLP]
    JACIII, 1999, v:3, n:5, pp:386-393 [Journal]
  25. Hisao Ishibuchi, Tadashi Yoshida, Tadahiko Murata
    Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling. [Citation Graph (0, 0)][DBLP]
    IEEE Trans. Evolutionary Computation, 2003, v:7, n:2, pp:204-223 [Journal]
  26. Hisao Ishibuchi, Tomoharu Nakashima, Tadahiko Murata
    Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems. [Citation Graph (0, 0)][DBLP]
    IEEE Transactions on Systems, Man, and Cybernetics, Part B, 1999, v:29, n:5, pp:601-618 [Journal]

  27. A Design of Problem Solving Environments for Policy Making Assistance Using MAS-Based Social Simulation. [Citation Graph (, )][DBLP]


  28. Many-Objective Optimization for Knapsack Problems Using Correlation-Based Weighted Sum Approach. [Citation Graph (, )][DBLP]


  29. Neighborhood structures for genetic local search algorithms. [Citation Graph (, )][DBLP]


  30. Simulating a Transition Process to Generation-based Funding Scheme in Public Pension Planning. [Citation Graph (, )][DBLP]


  31. Developing control table for multiple agents using GA-Based Q-learning with neighboring crossover. [Citation Graph (, )][DBLP]


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