Non-stationary function optimization using polygenic inheritance

Non-stationary function optimization has proved a difficult area for Genetic Algorithms. Standard haploid populations find it dimcult to track a moving target, and tend to converge on a local optimum that appears early in a run. It is generally accepted that diploid GAs can cope with these problems because they have a genetic memory, that is, genes that may be required in the future are maintained in the current population. This paper describes a haploid GA that appears to have this property, through the use of Polygenic Inheritance. Polygenic inheritance differs from most implementations of GAs in that several genes contribute to each phenotypic trait. Two non-stationary function optimization problems from the literature are described, and a number of comparisons performed. We show that Polygenic inheritance enjoys all the advantages normally associated with diploid structures, with none of the usual costs, such as complex crossover mechanisms, huge mutation rates or ambiguity in the mapping process.