A new genetic algorithm using large mutation rates and population-elitist selection (GALME)

Genetic algorithms (GAs) are promising for function optimization. Methods for function optimization are required to perform local search as well as global search in a balanced way. It is recognized that the traditional GA is not well suited to local search. I have tested algorithms combining various ideas to develop a new genetic algorithm to obtain the global optimum effectively. The results show that the performance of a genetic algorithm using large mutation rates and population-elitist selection (GALME) is superior. This paper describes the GALME and its theoretical justification, and presents the results of experiments, compared to the traditional GA. Within the range of the experiments, it turns out that the performance of GALME is remarkably superior to that of the traditional GA.