Global optimization based on genetic algorithms and evolution strategies

In this paper, a new algorithm for global optimization, based on genetic algorithms and evolution strategies, is presented. This class of algorithms is characterized by a stochastic search on sets of points and uses natural adaptive population ability. The proposed algorithm follows one which was first developed in the laboratory and used a genetic model for the optimization problem. The main difference lies in the modelling of individuals which first used the haploïd model. In the present work, a more evolved model is used, consisting of a diploid one. Following the description of the algorithm, a demonstration of asymptotic convergence is provided. Then, the influence of the parameters is evaluated showing the great importance of homozygocity rate and the nature of the dominance. A maximisation problem is finally carried out, and the performances with the two types of dominance are compared with those obtained through the intermediary of a classical and a hybrid genetic algorithm. In conclusion, this study shows the efficiency and potentialities of such an algorithm.