On the Genotype Compression and Expansion for Evolutionary Algorithms in the Continuous Domain

TL;DR

This paper explores how changing the size of genotypes through compression and expansion affects evolutionary algorithms' performance, finding that expansion often improves results by altering the search landscape.

Contribution

It introduces strategies for genotype compression and expansion and demonstrates that expansion enhances evolutionary algorithm effectiveness across various problems.

Findings

Genotype expansion outperforms compression in evolutionary algorithms.

Expansion modifies the genotype-phenotype mapping beneficially.

Results show improved optimization performance with expanded genotypes.

Abstract

This paper investigates the influence of genotype size on evolutionary algorithms' performance. We consider genotype compression (where genotype is smaller than phenotype) and expansion (genotype is larger than phenotype) and define different strategies to reconstruct the original variables of the phenotype from both the compressed and expanded genotypes. We test our approach with several evolutionary algorithms over three sets of optimization problems: COCO benchmark functions, modeling of Physical Unclonable Functions, and neural network weight optimization. Our results show that genotype expansion works significantly better than compression, and in many scenarios, outperforms the original genotype encoding. This could be attributed to the change in the genotype-phenotype mapping introduced with the expansion methods: this modification beneficially transforms the domain landscape and alleviates the search space traversal.