On the Influence of Selection Operators on Performances in Cellular Genetic Algorithms

TL;DR

This paper investigates how different levels of selective pressure affect the performance of cellular genetic algorithms, revealing that optimal performance depends on factors beyond just selective pressure measures.

Contribution

The study introduces two strategies to reduce selective pressure in cellular genetic algorithms and analyzes their impact on solving the quadratic assignment problem.

Findings

Optimal control parameter values improve performance

Selective pressure alone does not fully explain algorithm effectiveness

Cellular structure slows solution propagation, aiding diversity

Abstract

In this paper, we study the influence of the selective pressure on the performance of cellular genetic algorithms. Cellular genetic algorithms are genetic algorithms where the population is embedded on a toroidal grid. This structure makes the propagation of the best so far individual slow down, and allows to keep in the population potentially good solutions. We present two selective pressure reducing strategies in order to slow down even more the best solution propagation. We experiment these strategies on a hard optimization problem, the quadratic assignment problem, and we show that there is a value for of the control parameter for both which gives the best performance. This optimal value does not find explanation on only the selective pressure, measured either by take over time and diversity evolution. This study makes us conclude that we need other tools than the sole selective pressure measures to explain the performances of cellular genetic algorithms.