From Cells to Islands: An unified Model of Cellular Parallel Genetic Algorithms

TL;DR

This paper introduces an anisotropic selection scheme for cellular genetic algorithms, enabling dynamic control of diversity and selective pressure, effectively bridging cellular and island models to optimize difficult problems like the Quadratic Assignment Problem.

Contribution

The paper proposes a novel anisotropic selection scheme that unifies cellular and island genetic algorithms, allowing better control over diversity and migration for improved optimization.

Findings

Tuning anisotropic degree balances diversity and performance.

The scheme effectively adapts between cellular and island models.

Optimized trade-offs improve results on the Quadratic Assignment Problem.

Abstract

This paper presents the Anisotropic selection scheme for cellular Genetic Algorithms (cGA). This new scheme allows to enhance diversity and to control the selective pressure which are two important issues in Genetic Algorithms, especially when trying to solve difficult optimization problems. Varying the anisotropic degree of selection allows swapping from a cellular to an island model of parallel genetic algorithm. Measures of performances and diversity have been performed on one well-known problem: the Quadratic Assignment Problem which is known to be difficult to optimize. Experiences show that, tuning the anisotropic degree, we can find the accurate trade-off between cGA and island models to optimize performances of parallel evolutionary algorithms. This trade-off can be interpreted as the suitable degree of migration among subpopulations in a parallel Genetic Algorithm.