Tuning Selection Pressure in Tournament Selection

Selection pressure controls the selection of individuals from the current population to produce a new population in the next generation. It gives individuals of higher quality a higher probability of being used to create the next generation so that Evolutionary Algorithms (EAs) can focus on promising regions in the search space. An evolutionary learning process is dynamic and requires different selection pressures at different learning stages in order to speed up convergence or avoid local optima. Therefore, it desires selection mechanisms being able to automatically tune selection pressure during evolution. Tournament selection is a popular selection method in EAs. This paper focuses on tournament selection and shows that standard tournament selection is unaware of the dynamics in the evolutionary process thus is unable to tune selection pressure automatically. This paper then presents a novel approach which integrates the knowledge of the Fitness Rank Distribution (FRD) of a population into tournament selection. Through mathematical modelling, simulations and experimental study, this paper shows that the new approach is effective and using the knowledge of FRD is a promising way to modify the standard tournament selection method for tuning the selection pressure dynamically and automatically along evolution.