Issues of Binary Representation in Evolutionary Algorithms

Recent studies show that evolutionary algorithms are effective optimization tools for their success in solving real-world problem with complex and competing specifications. Although their performances are greatly influenced by the type of representation adopted, this choice often arises from intuition and guesswork due to the absence of proper guidelines and framework. This paper considers binary representation and presents a comprehensive study on its issues, identifying the key factors that affect its algorithmic performance. Furthermore, two metrics are proposed to generalize the concept of preservation which quantifies the similarities between the genotype and phenotype search space. The two classical translation codes i.e. binary and gray are studied based on the identified factors and a preservation analysis revealed the differences between them