Optimization of Cost Functions Using Evolutionary Algorithms with Local Learning and Local Search

Evolutionary algorithms can benefit from their association with local search operators, giving rise to hybrid or memetic algorithms. However, when dealing with costly functions, the cost of the local search may be prohibitive. We propose the use of local approximations in order to alleviate the computational burden of the local search phase of memetic algorithms for optimization with costly functions, as is the case in electromagnetic design. The results show the improvement achieved by the proposed combination of local learning and search within evolutionary algorithms