Hybrid Optimization Method Based on Genetic Algorithm and Cultural Algorithm

Knowledge about evolutionary information is not used in genetic algorithms effectively. Cultural algorithms with dual inheritance structure converge slowly because only mutation operator is adopted in the population space. A novel hybrid optimization method is proposed using genetic algorithm in population space. Four kinds of knowledge and two phases are abstracted. Steps of the algorithm are described in detail. Simulation results on the benchmark optimization functions indicate that the method converges faster than traditional cultural algorithms. In iteratively dynamic situation, results show that experience knowledge in the knowledge space is benefit to apperceive the change of situation and has the ability in memory, which increases the speed of convergence in a certain situation