A genetic algorithm with distance independent diversity control for high dimensional function optimization

Abstract

For genetic algorithms, it is important to maintain the population diversity. Some genetic algorithms have been proposed, which have an ability to control the diversity. But these algorithms use the distance between two individuals to control the diversity. Therefore, these performances become worse on ill-scaled functions. In this paper, we propose a new genetic algorithm, DIDC(a genetic algorithm with Distance Independent Diversity Control), that does not use a distance to control the population diversity. For controlling the diversity, DIDC uses two GAs that have different natures. For realizing different natures, one GA uses a crossover operator as a search operator, and the other GA uses a mutation operator in DIDC. By applying DIDC to several benchmark problems, we show that DIDC has a good performance on high dimensional, multimodal, non-separable and ill-scaled problems. Finally, we show that the control parameter of DIDC has the same effect on the search with the number of generating children n c.