GA-easy and GA-hard Constraint Satisfaction Problems

In this paper we discuss the possibilities of applying genetic algorithms (GA) for solving constraint satisfaction problems (CSP). We point out how the greediness of deterministic classical CSP solving techniques can be counterbalanced by the random mechanisms of GAs. We tested our ideas by running experiments on four diierent CSPs: N-queens, graph 3-colouring, the traac lights and the Zebra problem. Three of the problems have proven to be GA-easy, and even for the GA-hard one the performance of the GA could be boosted by techniques familiar in classical methods. Thus GAs are promising tools for solving CSPs. In the discussion, we address the issues of non-solvable CSPs and the generation of all the solutions. 1.1 Introduction In this paper we consider genetic algorithms (GA) for solving constraint satisfaction problems (CSP) with nite domains. The majority of CSP solving algorithms, which we will refer to as classical ones, are deterministic and constructive search algorithms. That is, solution { a member of the search space (being the direct product of the nite domains) { is constructed by step-by-step specifying a value for a still uninstantiated variable in such a way that all the constraints which can be evaluated, are satissed. If there is no such value for the current variable to be instantiated, then previous instantiations are revised (back-tracking). The selection of the variable to be instantiated and