An accelerated micro genetic algorithm for numerical optimization

In this paper, we present an accelerated micro genetic algorithm for numerical optimization. It is implemented by incorporating the conventional micro genetic algorithm with a local optimizer based on heuristic pattern move and Aitken Δ2 acceleration method. Performance tests with three benchmarking functions indicate that the presented algorithm has excellent convergence performance for multimodal optimization problems. The number of objective function evaluations required to obtain global optima is only 5.4-11.9% of that required by using conventional micro genetic algorithm.