A cultural algorithm for constrained optimization

Abstract

In this paper, we propose a technique that exploits knowledge extracted during the search to improve the performance of an evolutionary algorithm used for global optimization. The approach is based on a cultural algorithm combined with evolutionary programming and we show that produces highly competitive results at a relatively low computational cost.