Coevolutionary genetic algorithm for constraint satisfaction with a genetic repair operator for effective schemata formation

In this paper, we discuss on new Coevolutionary Genetic Algorithm for Constraint Satisfaction. Our basic idea is to explore effective genetic information in the population, i.e., schemata, and to exploit the genetic information in order to guide the population to better solutions. Our Coevolutiona,ry Genetic Algorithm (CGA) consists of two GA populations; the first GA, called ”H-GA”, searches for the solutions in a given environment (problem), and the second GA, called ”P-GA” , searches for effective genetic information involved in the H-GA, namely, good schemata. Thus, each individual in P-GA consists of alleles in H-GA or ”don’t care” symbol representing a schema in the HGA. These GA populations separately evolve in each genetic space at different abstraction levels and affect with each other by two genetic operators: ”superposition” and ”transcription”. We then applied our CGA to Constraint Satisfaction Problems (CSPs) incorporating a new stochastic ”repair” operator for P-GA to raise the consistency of schemata with the (local) constraint conditions in CSPs. We carried out two experiments: First, we examined the performance of CGA on various ”general” CSPs that are generated randomly for a wide variety of ”density” and ”tightness” of constraint conditons in the CSPs that are the basic measures of characterizing CSPs. Next, we examined ”structured” CSPs involving latent ”cluster” structures among the variables in the CSPs. For these experiments, computer. simulations confirmed us the effectiveness of our CGA.