Anisotropic selection in cellular genetic algorithms

TL;DR

This paper introduces Anisotropic Selection, a new scheme for cellular genetic algorithms that enhances diversity, controls selective pressure, and improves niche formation, with optimal parameters identified for problem-solving.

Contribution

The paper presents a novel anisotropic selection method for cGAs, demonstrating its benefits over traditional methods in diversity, niche formation, and performance optimization.

Findings

Anisotropic Selection controls selective pressure effectively.

It promotes niche emergence with low coupling and high cohesion.

Optimal anisotropic parameters improve problem-solving performance.

Abstract

In this paper we introduce a new selection scheme in cellular genetic algorithms (cGAs). Anisotropic Selection (AS) promotes diversity and allows accurate control of the selective pressure. First we compare this new scheme with the classical rectangular grid shapes solution according to the selective pressure: we can obtain the same takeover time with the two techniques although the spreading of the best individual is different. We then give experimental results that show to what extent AS promotes the emergence of niches that support low coupling and high cohesion. Finally, using a cGA with anisotropic selection on a Quadratic Assignment Problem we show the existence of an anisotropic optimal value for which the best average performance is observed. Further work will focus on the selective pressure self-adjustment ability provided by this new selection scheme.