Abstract: A high efficient evolutionary algorithm for function optimization is proposed, the algorithm uses hybrid selection strategy which combines tournament selection with roulette selection, classific mutations are employed in different individuals: pattern search aims the best individual so far, cauchy mutation aims to the individuals whose order based on fitness is one third of the population while ordinary mutation is employed in other individuals. The experiments of 15 benchmark problem show that the new algorithm can not only converge fastly, but also obtain the solution with high precision even if not the best.

摘要： 本文提出了一个求解函数优化问题的高效演化算法，其设计思想由混合选择策略与分类变异策略构成。该算法使用锦标赛选择、轮盘选择相结合的混合选择策略，变异运算分为三类进行：对最好个体实施模式搜索，对适应值排名靠前的三分之一的个体采用柯西变异，而其它个体使用普通变异算子。针对15个测试函数的实验取得了相当好的效果，实验结果表明该算法不仅收敛速度快，而且所求得的解达到或者以相当高的精度逼近最优解。