A new genetic algorithm with diploid chromosomes by using probability decoding for non-stationary function optimization

This paper proposes a new diploid operation technique with probability for non-stationary function optimization. The advantage of the technique over previous diploid genetoc algorithms, diploid GAs, is that one genotype is transformed into many phenotypes with probability. 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 make trade-off vetween the diversity and convergency.