Evolving Dynamic Change and Exchange of Genotype Encoding in Genetic Algorithms for Difficult Optimization Problems

TL;DR

This paper introduces a novel genetic algorithm variant called Split-and-Merge GA (SM-GA) that dynamically evolves genotype encoding to improve performance on difficult optimization problems, outperforming traditional static coding GAs.

Contribution

It proposes a dynamic exchange of genotype encoding in GAs using serial alternation strategies and a parallel dual coding framework, enhancing optimization effectiveness.

Findings

SM-GA significantly outperforms static single coding GAs.

Dynamic genotype encoding improves search efficiency.

Numerical experiments validate the effectiveness of the proposed method.

Abstract

The application of genetic algorithms (GAs) to many optimization problems in organizations often results in good performance and high quality solutions. For successful and efficient use of GAs, it is not enough to simply apply simple GAs (SGAs). In addition, it is necessary to find a proper representation for the problem and to develop appropriate search operators that fit well to the properties of the genotype encoding. The representation must at least be able to encode all possible solutions of an optimization problem, and genetic operators such as crossover and mutation should be applicable to it. In this paper, serial alternation strategies between two codings are formulated in the framework of dynamic change of genotype encoding in GAs for function optimization. Likewise, a new variant of GAs for difficult optimization problems denoted {\it Split-and-Merge} GA (SM-GA) is developed using a parallel implementation of an SGA and evolving a dynamic exchange of individual representation in the context of Dual Coding concept. Numerical experiments show that the evolved SM-GA significantly outperforms an SGA with static single coding.