A new genetic algorithm with diploid chromosomes using probability decoding for adaptation to various environments

This paper proposes a new diploid operation technique using probability for function optimization in nonstationary environments and describes a feature of diploid genetic algorithms (GAs). The advantage of the technique over previous diploid GAs is that one genotype is transformed into many phenotypes based on probability. This transformation is not made at random. It has a certain range of probabilities. Each individual has a range. The range allows adaptation to various environments. The technique allows genes to give a probabilistic representation of dominance, and can maintain the diversity of individuals. The experimental results show that the technique can adapt to severe environmental changes where previous diploid GAs cannot adapt. This paper shows that the technique can find optimum solutions with high probability and that the distribution of individuals changes when the environment changes. In addition, by comparing the proposed diploid GA with a haploid GA whose chromosome is twice the length, the features of the diploid are described. © 2010 Wiley Periodicals, Inc. Electron Comm Jpn, 93(8): 38–46, 2010; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecj.10097