An Adaptive Strategy Applied to Memetic Algorithms

Memetic algorithms(MAs) represent one of the promising areas of evolutionary algorithms. However, there are many issues to be solved to design a robust MA. In this paper, we introduce an adaptive memetic algorithm, named GADE-DHC, which combines a genetic algorithm and a differential evolution algorithm as global search methods with a directional hill climbing algorithm as local search method. In addition, a novel strategy is proposed to balance the intensity of global search methods and local search method, as well as the ratio between genetic algorithm and differential evolution algorithm. Experiments on several benchmark problems of diverse complexities have shown that the new approach is able to provide highly competitive results compared with other algorithms.