A Novel Binary Variable Representation for Genetic and Evolutionary Algorithms

Based on the theoretical guidance and existing recommendations for designing efficient genetic representations, we investigate a novel genetic representation - a splicing/decomposable (S/D) binary encoding in this paper. The S/D binary representation can be spliced and decomposed to describe potential solutions of the problem with different precisions by different number of uniform-salient building blocks (BBs). According to the characteristics of the S/D binary representation, genetic and evolutionary algorithms (GEAs) can be applied from the high scaled to the low scaled BBs sequentially to avoid genetic drift and improve GEAs´ performance. Our theoretical and empirical investigations reveal that the S/D binary representation is more proper than other existing binary encodings for GEAs searching.