Increasing Robustness of Binary-coded Genetic Algorithm

Jiangming Mao, Junichi Murata, Kotaro Hirasawa, Jinglu Hu

研究成果: Article査読

抄録

Genetic algorithms are often well suited for optimization problems because of their parallel searching and evolutionary ability. Crossover and mutation are believed to be the main exploration operators. In this paper, we focus on how crossover and mutation work in binary-coded genetic algorithm and investigate their effects on bit's frequency of population. According to the analysis of equilibrium of crossover, we can see the bit-based simulated crossover (BSC) is strong crossover method. Furthermore, to increase robustness of binary-coded genetic algorithm, multi-generation inheritance evolutionary strategy(MGIS) was proposed. Simulation results demonstrate the effectiveness of the proposed method.

本文言語English
ページ(範囲)1625-1630
ページ数6
ジャーナルIEEJ Transactions on Electronics, Information and Systems
123
9
DOI
出版ステータスPublished - 2003 1月

ASJC Scopus subject areas

  • 電子工学および電気工学

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