Boosting Genetic Algorithms with Self-Adaptive Selection

Abstract

In this paper we evaluate a new approach to selection in genetic algorithms (GAs). The basis of our approach is that the selection pressure is not a superimposed parameter defined by the user or some Boltzmann mechanism. Rather, it is an aggregated parameter that is determined collectively by the individuals in the population. We implement this idea in two different ways and experimentally evaluate the resulting genetic algorithms on a range of fitness landscapes. We observe that this new style of selection can lead to 30-40% performance increase in terms of speed.