ICHEA for Discrete Constraint Satisfaction Problems

Constraint satisfaction problem (CSP) is a subset of optimization problem where at least one solution is sought that satisfies all the given constraints. Presently, evolutionary algorithms (EAs) have become standard optimization techniques for solving unconstrained optimization problems where the problem is formalized for discrete or continuous domains. However, traditional EAs are considered ‘blind’ to constraint as they do not extract and exploit information from the constraints. A variation of EA – intelligent constraint handling for EA (ICHEA) proposed earlier models constraints to guide the evolutionary search to get improved and efficient solutions for continuous CSPs. As many real world CSPs have constraints defined in the form of discrete functions, this paper serves as an extension to ICHEA that reports its applicability for solving discrete CSPs. The experiment has been carried on a classic discrete CSP – the N-Queens problem. The experimental results show that extracting information from constraints and exploiting it in the evolutionary search makes the search more efficient. This provision is a problem independent formulation in ICHEA.