Exploring the Capability of Immune Algorithms: A Characterization of Hypermutation Operators

In this paper, an important class of hypermutation operators are discussed and quantitatively compared with respect to their success rate and computational cost. We use a standard Immune Algorithm (IA), based on the clonal selection principle to investigate the searching capability of the designed hypermutation operators. We computed the parameter surface for each variation operator to predict the best parameter setting for each operator and their combination. The experimental investigation in which we use a standard clonal selection algorithm with different hypermutation operators on a complex “toy problem”, the trap functions, and a complex NP-complete problem, the 2D HP model for the protein structure prediction problem, clarifies that only few really different and useful hypermutation operators exist, namely: inversely proportional hypermutation, static hypermutation and hypermacromutation operators. The combination of static and inversely proportional Hypermutation and hypermacromutation showed the best experimental results for the “toy problem” and the NP-complete problem.