Genetic algorithms for constraint satisfaction problems

Several approximate algorithms using hill-climbing techniques and neural networks have been proposed to solve large constraint satisfaction problems (CSPs) in a practical time. In these proposals, many methods of escaping from local optima are discussed, however, there are very few methods actively perform global search. In this paper we propose a hybrid search method that combines the genetic algorithm with the min-conflicts hill-climbing (MCHC). In our method, the individual that has the fewest conflicts in the population is used as the initial value of MCHC to search locally. The detailed experimental simulation is also performed to prove that the proposed method generally gives better efficiency than the random restarting MCHC when CSPs are sparsely-connected