A comparative study on the performance of dissortative mating and immigrants-based strategies for evolutionary dynamic optimization

摘要

Traditional Genetic Algorithms (GAs) mating schemes select individuals for crossover independently of their genotypic or phenotypic similarities. In Nature, this behavior is known as random mating. However, non-random protocols, in which individuals mate according to their kinship or likeness, are more common in natural species. Previous studies indicate that when applied to GAs, dissortative mating - a type of non-random mating in which individuals are chosen according to their similarities - may improve their performance (on both speed and reliability). Dissortative mating maintains genetic diversity at a higher level during the run, a fact that is frequently observed as a possible cause of dissortative GAs' ability to escape local optima. Dynamic optimization demands a special attention when designing and tuning a GA, since diversity plays an even more crucial role than it does when tackling static ones. This paper investigates the behavior of the Adaptive Dissortative Mating GA (ADMGA) in dynamic problems and compares it to GAs based on random immigrants. ADMGA selects parents according to their Hamming distance, via a self-adjustable threshold value. The method, by keeping population diversity during the run, provides an effective means to deal with dynamic problems. Tests conducted with dynamic trap functions and dynamic versions of Road Royal and knapsack problems indicate that ADMGA is able to outperform other GAs on a wide range of tests, being particularly effective when the frequency of changes is low. Specifically, ADMGA outperforms two state-of-the-art algorithms on many dynamic scenarios. In addition, and unlike preceding dissortative mating GAs and other evolutionary techniques for dynamic optimization, ADMGA self-regulates the intensity of the mating restrictions and does not increase the set of parameters in GAs, thus being easier to tune.