  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.
