  Traditional Genetic Algorithms (GAs) mating schemes select individuals for
crossover independently of their genotypic or phenotypic similarities. In
Nature, this behaviour is known as random mating. However, non-random schemes -
in which individuals mate according to their kinship or likeness - are more
common in natural systems. Previous studies indicate that, when applied to GAs,
negative assortative mating (a specific type of non-random mating, also known
as dissortative mating) may improve their performance (on both speed and
reliability) in a wide range of problems. Dissortative mating maintains the
genetic diversity at a higher level during the run, and that fact is frequently
observed as an explanation for dissortative GAs ability to escape local optima
traps. Dynamic problems, due to their specificities, demand special care when
tuning a GA, because diversity plays an even more crucial role than it does
when tackling static ones. This paper investigates the behaviour of
dissortative mating GAs, namely the recently proposed Adaptive Dissortative
Mating GA (ADMGA), on dynamic trap functions. 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 deceptive and nearly deceptive
trap functions indicate that ADMGA is able to outperform other GAs, some
specifically designed for tracking moving extrema, on a wide range of tests,
being particularly effective when speed of change is not very fast. When
comparing the algorithm to a previously proposed dissortative GA, results show
that performance is equivalent on the majority of the experiments, but ADMGA
performs better when solving the hardest instances of the test set.
