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 Coevolutionary 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. Two kinds of the fitness evaluation methods for the P-GA are introduced. We then applied our CGA to Constraint Satisfaction Problems (CSPs) incorporating a stochastic “repair” operator for the P-GA to raise the consistency of schemata with the (local) constraint conditions in CSPs. Various computer simulations on general CSPs, dynamic CSPs and cluster-structured CSPs elucidate the effectiveness of our approach.
