Research on Cultural Algorithm for Solving Nonlinear Constrained Optimization
求解约束优化问题的文化算法研究

The key idea behind cultural algorithm(CA)is to explicitly acquire problem-solving knowledge(beliefs)from the evolving population and in return apply that knowledge to guide the search.In this paper,we propose a CA based on mul- tilayer belief spaces that selects the best belief space from the multilayer belief spaces so as to apply the extracted knowledge to improve the performance of evolutionary algorithm used for constrained optimization.Examples show that the algorithm produces highly competitive results at a relatively low compu- tational cost.