A coevolutionary approach to adapt the genotype-phenotype map in genetic algorithms

Abstract

This article introduces a coevolutionary approach to genetic algorithms (GAs) for exploring not only within a part of the solution space defined by the genotype-pheno-type map, but also the map itself. In canonical GAs with a fixed map, how large an area of the solution space can be covered by possible genomes, and consequently how better solutions can be found by a GA, rely on how well the geotype-phenotype map in designed, but it is difficult for designers of the algorithms to design the map without a priori knowledge of the solution space. In the proposed algorithm, the genotype-phenotype map is improved adaptively during the search process for solution candidates. It is applied to 3-bit deceptive problems such as of typical combinatorial optimazation problems. These are well known because their difficulty for GAs can be controlled by the genotype-phenotype map, and this shows a fairly good performance compared with a conventional GA.