Using Dissortative Mating Genetic Algorithms to Track the Extrema of Dynamic Deceptive Functions

TL;DR

This paper demonstrates that the Adaptive Dissortative Mating Genetic Algorithm (ADMGA), which maintains diversity through self-adjustable parent selection based on Hamming distance, effectively tracks extrema in dynamic deceptive functions, outperforming other GAs especially in challenging scenarios.

Contribution

The paper introduces and evaluates ADMGA, a novel dissortative mating GA with self-adjustable thresholds, showing its superior performance in dynamic trap functions compared to existing methods.

Findings

ADMGA maintains higher diversity during runs.

ADMGA outperforms other GAs on dynamic trap functions.

ADMGA is particularly effective in slow-changing environments.

Abstract

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.