An evolutionary algorithm for continuous global optimization

In this paper, we study an evolutionary algorithm for continuous global optimization problems. Based on family competition and adaptive rules, the proposed approach integrates decreasing-based mutations and self-adaptive mutations to act global and local strategies, respectively. Our approach applied to six widely used functions. Experimental results indicate that in all tested problem, our approach performs more robust than other well-known evolutionary algorithms. Moreover, we discuss implementation details and essential components in our approach are analyzed.