Weighted constraint satisfaction problems are difficult optimization problems that could model applications from various domains. Evolutionary algorithms are not the first option for solving such type of problems. In this work, the evolutionary algorithm uses the information extracted from the previous best solutions to guide the search in the next iterations. After the archive of previous best solutions has been sufficiently (re)filled, a data mining module is called to find association rules between variables and values. The generated rules are used to improve further the search process. Different methods of applying the association rules are investigated. Computational experiments are done on academic and real-world problem instances. The obtained results validate the approach and show that it is competitive with existing approaches in literature.
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