Software reliability growth model for testing-domain. - Hiroshi Ohtera; Shigeru Yamada; Hiroyuki Narihisa (Syst. Comput. Jpn., 1991)

A New Iterated Local Search Algorithm Using Genetic Crossover for the Traveling Salesman Problem. - Kengo Katayama; Hiroyuki Narihisa (SAC, 1999)

Performance analysis for crossover operators of genetic algorithm. - Kengo Katayama; Hisayuki Hirabayashi; Hiroyuki Narihisa (Syst. Comput. Jpn., 1999)

Solving Large Binary Quadratic Programming Problems by Effective Genetic Local Search Algorithm. - Kengo Katayama; Masafumi Tani; Hiroyuki Narihisa (GECCO, 2000)

Predicting Chaotic Time Series by Ensemble Self-Generating Neural Networks. - Hirotaka Inoue; Hiroyuki Narihisa (IJCNN (2), 2000)

Improving Generalization Ability of Self-Generating Neural Networks Through Ensemble Averaging. - Hirotaka Inoue; Hiroyuki Narihisa (PAKDD, 2000)

On fundamental design of parthenogenetic algorithm for the binary quadratic programming problem. - Kengo Katayama; Hiroyuki Narihisa (CEC, 2001)

Efficient Hybrid Neural Network for Chaotic Time Series Prediction. - Hirotaka Inoue; Yoshinobu Fukunaga; Hiroyuki Narihisa (ICANN, 2001)

Parallel and Distributed Mining with Ensemble Self-Generating Neural Networks. - Hirotaka Inoue; Hiroyuki Narihisa (ICPADS, 2001)

Performance of simulated annealing-based heuristic for the unconstrained binary quadratic programming problem. - Kengo Katayama; Hiroyuki Narihisa (Eur. J. Oper. Res., 2001)

Optimizing a Multiple Classifier System. - Hirotaka Inoue; Hiroyuki Narihisa (PRICAI, 2002)

Effective Pruning Method for a Multiple Classifier System Based on Self-Generating Neural Networks. - Hirotaka Inoue; Hiroyuki Narihisa (ICANN, 2003)

Improving Performance of a Multiple Classifier System Using Self-generating Neural Networks. - Hirotaka Inoue; Hiroyuki Narihisa (Multiple Classifier Systems, 2003)

Self-organizing Neural Grove: Efficient Multiple Classifier System Using Pruned Self-generating Neural Trees. - Hirotaka Inoue; Hiroyuki Narihisa (PPSN, 2004)

Solving the maximum clique problem by k-opt local search. - Kengo Katayama; Akihiro Hamamoto; Hiroyuki Narihisa (SAC, 2004)

Efficiency of Parallel Exponential Evolutionary Programming. - Hiroyuki Narihisa; Takahiro Taniguchi; Michiaki Thuda; Kengo Katayama (ICPP Workshops, 2005)

Self-organizing neural grove and its applications. - Hirotaka Inoue; Hiroyuki Narihisa (IJCNN, 2005)

Self-organizing neural grove: effective multiple classifier system with pruned self-generating neural trees. - Hirotaka Inoue; Hiroyuki Narihisa (ISCAS (3), 2005)

Reinforcement learning agents with primary knowledge designed by analytic hierarchy process. - Kengo Katayama; Takahiro Koshiishi; Hiroyuki Narihisa (SAC, 2005)

An effective local search for the maximum clique problem. - Kengo Katayama; Akihiro Hamamoto; Hiroyuki Narihisa (Inf. Process. Lett., 2005)

Parallel performance of ensemble self-generating neural networks for chaotic time series prediction problems. - Hirotaka Inoue; Hiroyuki Narihisa (Syst. Comput. Jpn., 2005)

Evolutionary Programming With Only Using Exponential Mutation. - Hiroyuki Narihisa; Keiko Kohmoto; Takahiro Taniguchi; Mayumi Ohta; Kengo Katayama (IEEE Congress on Evolutionary Computation, 2006)

Exponential Evolutionary Programming Without Self-Adaptive Strategy Parameter. - Hiroyuki Narihisa; Takahiro Taniguchi; Mayumi Ohta; Kengo Katayama (IEEE Congress on Evolutionary Computation, 2006)

Variable depth search and iterated local search for the node placement problem in multihop WDM lightwave networks. - Kengo Katayama; Hiroshi Yamashita; Hiroyuki Narihisa (IEEE Congress on Evolutionary Computation, 2007)

Iterated k-Opt Local Search for the Maximum Clique Problem. - Kengo Katayama; Masashi Sadamatsu; Hiroyuki Narihisa (EvoCOP, 2007)

Efficient Incremental Learning Using Self-Organizing Neural Grove. - Hirotaka Inoue; Hiroyuki Narihisa (ICONIP (1), 2007)

Efficient Incremental Learning with Self-Organizing Neural Grove. - Hirotaka Inoue; Hiroyuki Narihisa (DMIN, 2008)