Our research has been focused on developing techniques for solving binary constraint satisfaction problems (CSP) using evolutionary algorithms, which take into account the constraint graphs topology. In this paper, we introduce a new idea to improve the performance of evolutionary algorithms, that solve complex problems. It is inspired from a real world observation: The ability to evolve for an individual in an environment that changes is not only related to its genetic material. It also comes from what has learned from it parents. The key idea of this paper is to use its inheritance to dynamically improve the way the algorithm creates a new population using a given set of operators. This new dynamic operator selection strategy has been applied to an evolutionary algorithm to solve CSPs, but can be easily extended to other class of evolutionary algorithms. A set of benchmarks shows how the new strategy can help to solve large NP-hard problems with the 3-graph coloring example.