The evolutionary algorithms find difficulties in solving constraint satisfaction problems. The paper verifies if such algorithms could improve their results by using data mining techniques. The proposed approach uses association rules mining to guide the evolutionary search. The association rules are found from the past experience of the algorithm and are applied on individuals in order to keep the good direction and to improve them. A new escaping local optima strategy is proposed based on the mined rules. The considered problems to be solved are over-constrained constraint satisfaction problems where the number of satisfied constraints must be maximized. Results on randomly generated binary Max-CSP instances and on real world problems are given.
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