A New Genetic Algorithm with Diploid Chromosomes by Using Probability Decoding for Adaptation to Various Environments

Abstract

This paper proposes a new diploid operation technique with probability for function optimization under non-stationary environments and describes a feature of diploid genetic algorithms, diploid GAs. The advantage of the technique over previous diploid GAs is that one genotype is transformed into many phenotypes with probability. This transformation is not made at random. It has a certain range of probabilistic. Each individual has each range. The range enables to adapt to various environments. The technique allows genes probabilistic representation of dominance, and can keep a diversity of individuals. The experiment results show that the technique can adapt to severe environmental changes where previous diploid GAs cannot adapt. It is shown that the technique is able to find optimum solutions with high probability and that a distribution of individuals changes when the environment changes. Moreover, by comparing proposed diploid GA with haploid GA whose chromosome is twice the length, a feature of diploid is described.