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AuthorK. S. Anderson and Y. Hsu
TitleCrossover Strategy for Enhanced Solution Space Exploration of Dynamic Systems
Year1998
AbstractKey aspects of Genetic Algorithms (GAs) as flexible and powerful function optimizers are their domain independent technique which are not restricted to particular problem formats and their ability to generate and test various parameter combination efficiently. However, GAs using a more traditional single-point crossover strategy to perform parameter recombination exhibit several flaws which greatly restrain the GAs exploration capability. This paper presents a family of alternative crossover strategies, which emphasize parameter recombination so to enhance solution space exploration, yet are not so disruptive as to defeat the local exploitation aspects of the algorithm. The empirical results show that GAs with the proposed crossover strategies can identify optimal or near optimal regions of the design hyperplane with fewer generations than traditional single-point crossover.