Evolutionary search guided by the constraint network to solve CSP

We are interested in defining a general evolutionary algorithm to solve constraint satisfaction problems, which takes into account both advantages of the systematic and traditional methods and of characteristics of the CSP. In this context knowledge about properties of the constraint network has allowed us to define a fitness function, for evaluation (Riff, 1996). We introduce two new operators which look at the constraint network during evolution. The first one is a bisexual operator like crossover denominated arc-crossover, for exploitation. The second one is an operator like mutation called arc-mutation, for exploration. These operators are used to improve the stochastic search.