A Coevolutionary Approach To Adapt The Genotype-Pheonotype Map In Genetic Algorithms

This paper introduces a coevolutionary approach to genetic algorithms (GAs) for exploring not only wihtin a part of the solution space defined by the genotype-phenotype map but also the map itself. In canonical GAs with the fixed map, how large 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 genotypephenotype map is designed, but it is difficult for designers of the algorithms to designe the map without a-priori knowledge of the solution space. In the proposed algorithm, the genotype-phenotype map is improved adaptively during the searching process for solution candidates. It is applied to 3-bit deceptive problems as a kind of typical combinatorial optimization problems, which are well-known by that the diffculty against GAs can be controlled by the genotypephenotype map, and shows fairly good performance beside a conventional GA.