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This paper investigates the influence of genotype size on evolutionaryalgorithms' performance. We consider genotype compression (where genotype issmaller than phenotype) and expansion (genotype is larger than phenotype) anddefine different strategies to reconstruct the original variables of thephenotype from both the compressed and expanded genotypes. We test our approachwith several evolutionary algorithms over three sets of optimization problems:COCO benchmark functions, modeling of Physical Unclonable Functions, and neuralnetwork weight optimization. Our results show that genotype expansion workssignificantly better than compression, and in many scenarios, outperforms theoriginal genotype encoding. This could be attributed to the change in thegenotype-phenotype mapping introduced with the expansion methods: thismodification beneficially transforms the domain landscape and alleviates thesearch space traversal.

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