Tracking Extrema in Dynamic Fitness Functions with Dissortative Mating Genetic Algorithms

This paper investigates the behavior of the adaptive dissortative mating genetic algorithm (ADMGA) on dynamic problems and compares it with other genetic algorithms (GA). ADMGA is a non-random mating algorithm that selects parents according to their Hamming distance, via a self-adjustable threshold value. The resulting method, by keeping population diversity during the run, provides new means for GAs to deal with dynamic problems, which demand high diversity in order to track the optima. Tests conducted on combinatorial and trap functions indicate that ADMGA is more robust than traditional GAs and it is capable of outperforming a previously proposed dissortative scheme on a wide range of tests.