Complementary crossover for genetic software architecture synthesis

Techniques exist to synthesize software architecture using genetic algorithms that employ transformations based on mutations and crossover. In this paper, we demonstrate that complementary crossover can significantly improve this technique. We study two versions of complementary crossover, one in which parents are selected so that they complement each other but the genes are inherited randomly from the parents, and another in which the genes are inherited in a more purposeful way. Empirical studies on two sample systems suggest that the complementary crossover outperforms the traditional crossover in genetic software architecture synthesis especially in the presence of mutations that provide delayed reward.