A hybrid approach of EDAs and GAs based on master/slave cooperation for continuous function optimization

This paper proposes a hybrid method of estimation of distribution algorithms (EDAs) and genetic algorithms (GAs) based on master/slave cooperation. The master process estimates the probability distribution of the search space based on the non-dependency model at each iteration and sends probability vectors to slaves. The slaves use the vector to generate new initial population. Our approach employs the simplest probability model but compensates for the accuracy problems by applying GAs to the solutions sampled from the simplest model. Moreover, our method can be incorporated with searching strategy and also easily parallelized. Computer experiment shows some effectiveness of our method.