Normalization for Genetic Algorithms With Nonsynonymously Redundant Encodings

Normalization transforms one parent genotype to be consistent with the other before crossover. In this paper, we explain how normalization alleviates the difficulties caused by nonsynonymously redundant encodings in genetic algorithms. We define the encodings with maximally nonsynonymous property and prove that the encodings induce uncorrelated search spaces. Extensive experiments for a number of problems show that normalization transforms the uncorrelated search spaces to correlated ones and leads to significant improvement in performance.