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

This paper introduces a coevolutionary approach to genetic algorithms (GAs) for exploring not only within 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 a 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 genotype-phenotype map is 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 searching process for solution candidates. It is applied to 3-bit deceptive problems as a kind of typical combinatorial optimization problem, which are well-known in that the difficulty against GAs can be controlled by the genotype-phenotype map, and shows fairly good performance beside a conventional GA