Eliminating Positional Dependency in Binary Representation via Redundancy

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 study on the key factors that affect its algorithmic performance. Subsequently, an encoding scheme is proposed to resolve the problem of positional dependency in binary coding, which is the commonly used genotype-phenotype mapping for this representation. This is achieved by introducing redundancy into the genotype-phenotype mapping, which will better preserve the similarities between the genotype and phenotype search space by resolving the exponential orderings between the alleles. Theoretical analysis and empirical study were conducted to investigate the characteristics of the proposed representation