Polynomial Selection Scheme with Dynamic Parameter Estimation in Cellular Genetic Algorithm

Recent study has introduced the powerful selection scheme in cellular genetic algorithm that can produce all ranges of selective pressure. The parameters used in that study, however, are empirically estimated by numbers of experiments. In this study, we propose the idea of performing a parameter estimation from a theoretical perspective. In the concept of maximizing the probability to find the new best solution together with hill-climbing optimization, enabling search for an optimal parameter in each generation. The selection scheme with the optimal parameter yields the numbers of mating that maximizes the probability of finding better solutions. This optimal parameter changes during run and it is adaptive to the behavior of a particular evolution. In order to confirm the capability of this parameter estimation method, we have conducted experiments to compare the manually tuned static parameter and the estimated dynamic parameter obtained from this method. Result from the experiment shows that the algorithm with estimated parameter performed better than the former method, even with the best tuned parameter. Therefore, by applying this parameter estimation to the selection scheme stated at the beginning, we would be able to create a new universal adaptive paradigm for the cellular evolutionary algorithm.