Fuzzy cultural algorithms with evolutionary programming for real-valued function optimization.

摘要

The Fuzzy Cultural Algorithm is the Cultural Algorithm with the fuzzy belief space and the fuzzy interface.; Cultural Algorithm itself is a computational self-adaptive model which Consist of a population and a belief space. Problem solving experience of individuals selected from the population space by the acceptance function is generalized and stored in the belief space. This knowledge can then control the evolution of the population component by means of the influence function.; We use the fuzzy classification to the fuzzy acceptance function , the L_R type of the fuzzy number to the normative knowledge in the belief space, and the fuzzy upper boundary membership to the influence function.; We implemented 12 functions to compare with the FAM in experiments. We got the great satisfaction in improvements for (1){A0}convergence speed; (2) CPU time; (3){A0}success ratio for a given set of the function minimization problems. In particular, we compare various approaches using normative knowledge in guiding the search process. Also we investigate the impact of different acceptance and influence function on the system's performance by employing different fuzzy approaches.