Sharing evolution genetic algorithm for global numerical optimization
In this paper, a sharing evolution genetic algorithms (SEGA) is proposed to solve global numerical optimization problems. In the SEGA, three strategies are proposed, which are population manager, sharing cross-over and sharing mutation, for effective increasing new born offspring's solution searching ability. Experiments were conducted on CEC-05 benchmark problems which included unimodal, multimodal, expanded, and hybrid composition functions. The results showed that the proposed method exhibits better performance when solving these benchmark problems compared to recent variants of the genetic algorithms.