A discussion on real number vector representation, generation alternation models and effect by division of population of genetic algorithm

摘要

Recently, many Genetic Algorithm (GA) schemes have been developed to find good results for problems. Among them, this paper focused on Real-coded Genetic Algorithm, Generation Alternation Model, and Island Model. Real-coded GA is much effective for continuous function optimization than Bitstring GA. Minimal Generation Gap(MGG), one of the generation alternation models, is able to prevent premature convergence. Island model, one of the parallel models for GA, provides the better solutions than the conventional GA with a single population. Discussed in this paper is performance of real-coded GA with MGG and the island model. The examined model is applied to some test functions to verify the effectiveness of each scheme. Numerical results show that real-coded GA with multiple sub-populations provides higher search ability those that with a single population.