Learning to be selective in genetic-algorithm-based design optimization

摘要

In this paper we describe a method for improving genetic-algorithm-based optimization using search control. The idea is to utilize the sequence of points explored during a search to guide further exploration. The proposed method is particularly suitable for continuous spaces with expensive evaluation functions, such as arise in engineering design. Empirical results in several engineering design domains demonstrate that the proposed method can significantly improve the efficiency and reliability of the GA optimizer.