Maintaining diversity through adaptive selection, crossover and mutation

This paper presents an Adaptive Genetic Algorithm (AGA) where selection pressure, crossover and mutation probabilities are adapted according to population diversity statistics. The creation and maintenance of a diverse population of healthy individuals is a central goal of this research. To realise this objective, population diversity measures are utilised by the parameter adaptation process to both explore (through diversity promotion) and exploit (by local search and maintenance of a presence in known good regions of the fitness landscape). The performance of the proposed AGA is evaluated using a multi-modal, multi-dimensional function optimisation benchmark. Results presented indicate that the AGA achieves better fitness scores faster compared to a traditional GA.