Evolutionary algorithms have been efficiently used for solv-ing combinatorial problems. However a successful application rely on a good definition of the algorithm structure and a correct search guidance. Similarly to the majority of metaheuristic methods, the performance of an evolutionary algorithm is intrinsically linked to its ability to properly manage the balance between the exploitation and the exploration ofthe search space. Recently, new approaches have emerged to make thesealgorithms more independent, especially by automating the setting ofparameters. We propose a new approach whose objective is twofold: (1) to manage an important set of potential operators, whose performances are a priori unknown, and (2) to dynamically control the behavior of operators in a evolutionary algorithm, thanks to their probabilities of application.

http://dx.doi.org/10.1007/978-3-642-14156-0