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Hiroyuki Narihisa: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Kengo Katayama, Masashi Sadamatsu, Hiroyuki Narihisa
    Iterated k-Opt Local Search for the Maximum Clique Problem. [Citation Graph (0, 0)][DBLP]
    EvoCOP, 2007, pp:84-95 [Conf]
  2. Kengo Katayama, Masafumi Tani, Hiroyuki Narihisa
    Solving Large Binary Quadratic Programming Problems by Effective Genetic Local Search Algorithm. [Citation Graph (0, 0)][DBLP]
    GECCO, 2000, pp:643-650 [Conf]
  3. Hirotaka Inoue, Yoshinobu Fukunaga, Hiroyuki Narihisa
    Efficient Hybrid Neural Network for Chaotic Time Series Prediction. [Citation Graph (0, 0)][DBLP]
    ICANN, 2001, pp:712-718 [Conf]
  4. Hirotaka Inoue, Hiroyuki Narihisa
    Effective Pruning Method for a Multiple Classifier System Based on Self-Generating Neural Networks. [Citation Graph (0, 0)][DBLP]
    ICANN, 2003, pp:11-18 [Conf]
  5. Hirotaka Inoue, Hiroyuki Narihisa
    Parallel and Distributed Mining with Ensemble Self-Generating Neural Networks. [Citation Graph (0, 0)][DBLP]
    ICPADS, 2001, pp:423-428 [Conf]
  6. Hiroyuki Narihisa, Takahiro Taniguchi, Michiaki Thuda, Kengo Katayama
    Efficiency of Parallel Exponential Evolutionary Programming. [Citation Graph (0, 0)][DBLP]
    ICPP Workshops, 2005, pp:588-595 [Conf]
  7. Hirotaka Inoue, Hiroyuki Narihisa
    Predicting Chaotic Time Series by Ensemble Self-Generating Neural Networks. [Citation Graph (0, 0)][DBLP]
    IJCNN (2), 2000, pp:231-236 [Conf]
  8. Hirotaka Inoue, Hiroyuki Narihisa
    Self-organizing neural grove: effective multiple classifier system with pruned self-generating neural trees. [Citation Graph (0, 0)][DBLP]
    ISCAS (3), 2005, pp:2502-2505 [Conf]
  9. Hirotaka Inoue, Hiroyuki Narihisa
    Improving Performance of a Multiple Classifier System Using Self-generating Neural Networks. [Citation Graph (0, 0)][DBLP]
    Multiple Classifier Systems, 2003, pp:256-265 [Conf]
  10. Hirotaka Inoue, Hiroyuki Narihisa
    Improving Generalization Ability of Self-Generating Neural Networks Through Ensemble Averaging. [Citation Graph (0, 0)][DBLP]
    PAKDD, 2000, pp:177-180 [Conf]
  11. Hirotaka Inoue, Hiroyuki Narihisa
    Self-organizing Neural Grove: Efficient Multiple Classifier System Using Pruned Self-generating Neural Trees. [Citation Graph (0, 0)][DBLP]
    PPSN, 2004, pp:1113-1122 [Conf]
  12. Hirotaka Inoue, Hiroyuki Narihisa
    Optimizing a Multiple Classifier System. [Citation Graph (0, 0)][DBLP]
    PRICAI, 2002, pp:285-294 [Conf]
  13. Kengo Katayama, Akihiro Hamamoto, Hiroyuki Narihisa
    Solving the maximum clique problem by k-opt local search. [Citation Graph (0, 0)][DBLP]
    SAC, 2004, pp:1021-1025 [Conf]
  14. Kengo Katayama, Takahiro Koshiishi, Hiroyuki Narihisa
    Reinforcement learning agents with primary knowledge designed by analytic hierarchy process. [Citation Graph (0, 0)][DBLP]
    SAC, 2005, pp:14-21 [Conf]
  15. Kengo Katayama, Hiroyuki Narihisa
    A New Iterated Local Search Algorithm Using Genetic Crossover for the Traveling Salesman Problem. [Citation Graph (0, 0)][DBLP]
    SAC, 1999, pp:302-306 [Conf]
  16. Kengo Katayama, Akihiro Hamamoto, Hiroyuki Narihisa
    An effective local search for the maximum clique problem. [Citation Graph (0, 0)][DBLP]
    Inf. Process. Lett., 2005, v:95, n:5, pp:503-511 [Journal]
  17. Kengo Katayama, Hisayuki Hirabayashi, Hiroyuki Narihisa
    Performance analysis for crossover operators of genetic algorithm. [Citation Graph (0, 0)][DBLP]
    Systems and Computers in Japan, 1999, v:30, n:2, pp:20-30 [Journal]
  18. Hirotaka Inoue, Hiroyuki Narihisa
    Parallel performance of ensemble self-generating neural networks for chaotic time series prediction problems. [Citation Graph (0, 0)][DBLP]
    Systems and Computers in Japan, 2005, v:36, n:10, pp:82-92 [Journal]

  19. Efficient Incremental Learning with Self-Organizing Neural Grove. [Citation Graph (, )][DBLP]


  20. Efficient Incremental Learning Using Self-Organizing Neural Grove. [Citation Graph (, )][DBLP]


  21. Variable depth search and iterated local search for the node placement problem in multihop WDM lightwave networks. [Citation Graph (, )][DBLP]


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