A Novel Mating Approach for Genetic Algorithms

Genetic algorithms typically use crossover, which relies onmating a set of selected par-ents. As part of crossover, random mating is often carried out. A novel approach to parent mating is presented in this work. Our novel approach can be applied in combi-nation with a traditional similarity-based criterion to measure distance between indi-viduals or with a fitness-based criterion. We introduce a parameter called mating index that allows different mating strategies to be developed within a uniform framework: from an exploitative strategy called BEST-FIRST to an explorative one called BEST-LAST. SELF-ADAPTIVE mating is defined in the context of the novel algorithm in order to achieve a balance between exploitation and exploration in a domain-independent manner. The present work formally defines the novel mating approach, analyzes its behavior, and conducts an extensive experimental study to quantitatively determine its benefits. In the domain of real function optimization, the experiments show that, as the degree of multimodality of the function at hand grows, it is convenient to in-crease the mating index in order to obtain good performance. In the case of the SELF-ADAPTIVE mating strategy, the experiments give good results for a significant set of the studied cases