Memetic algorithm, as a hybrid strategy, is an intelligent optimization method in problem solving. These algorithms are similar in nature to genetic algorithms as they follow evolutionary strategies, but they also incorporate a refinement phase during which they learn about the problem and search space. The efficiency of these algorithms depends on the nature and architecture of the imitation operator used. In this paper a novel adaptive memetic algorithm has been developed in which the influence factor of environment on the learning abilities of each individual is set adaptively. This translates into a level of autonomous behavior, after a while that individuals gain some experience. Simulation results on benchmark function proved that this adaptive approach can increase the quality of the results and decrease the computation time simultaneously.  The adaptive memetic algorithm proposed in this paper also shows better stability when compared with the classic memetic algorithm.