Local learning and search in memetic algorithms

The use of local search in evolutionary techniques is believed to enhance the performance of the algorithms, giving rise to memetic or hybrid algorithms. However, in many continuous optimization problems the additional cost required by local search may be prohibitive. Thus we propose the local learning of the objective and constraint functions prior to the local search phase of memetic algorithms, based on the samples gathered by the population through the evolutionary process. The local search operator is then applied over this approximated model. We perform some experiments by combining our approach with a real-coded genetic algorithm. The results demonstrate the benefit of the proposed methodology for costly black-box functions.