Coevolutionary Genetic Algorithms for solving Dynamic Constraint Satisfaction Problems

Abstract

In this paper, we discuss the adaptability of Coevolutionary Genetic Algorithms on dynamic environments. Our CGA consists of two populations: solution-level one and schema-level one. The solution-level population searches for the good solution in a given problem. The schema-level population searches for the good schemata in the former population. Our CGA performs effectively by exchanging genetic information between these populations. Also, we define Dynamic Constraint Satisfaction Problems as such dynamic environments. General CSPs are defined by two stochastic parameters: density and tightness, then, Dynamic CSPs are defined as a sequence of static constraint networks of General CSPs. Computational results on DCSPs confirm us the effectiveness of our approach.