On the Genotype Compression and Expansion for Evolutionary Algorithms in the Continuous Domain
This work addresses performance optimization for evolutionary algorithms in continuous domains, offering incremental improvements in search efficiency.
The paper tackled the problem of how genotype size affects evolutionary algorithm performance by testing compression and expansion strategies, finding that genotype expansion significantly outperforms compression and often the original encoding, with improvements demonstrated on benchmark functions, physical unclonable functions, and neural network optimization.
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.