Increasing Robustness of Binary-coded Genetic Algorithm

Jiangming Mao, Junichi Murata, Kotaro Hirasawa, Jinglu Hu

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)1625-1630
Number of pages6
JournalIEEJ Transactions on Electronics, Information and Systems
Issue number9
Publication statusPublished - Jan 2003

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering


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