一种求解数值优化问题的进化规划算法  

Evolutionary Programming Algorithm for Solving Numerical Optimization Problems

作　　者：谢波[1] 余永权[1] 

机构地区：[1]广东工业大学计算机学院,广州510006

出　　处：《计算机工程》2011年第13期150-152,共3页Computer Engineering

基　　金：国家自然科学基金资助项目(60272089);广东省自然科学基金资助项目(980406)

摘　　要：针对进化规划算法收敛速度慢、容易早熟收敛等问题,提出一种基于探测变异的进化规划算法。该算法通过降维得到多个探测变异量,对个体进行探测变异,使个体始终向适应度好的方向进化,并利用自适应高斯变异标准差伸缩搜索空间,使个体跳出局部最优解。通过3个经典算例对其性能进行测试,实验结果证明该算法收敛速度快,求解质量高,可以解决早熟收敛等问题。Aiming at the problems of Evolutionary Programming（EP） such as premature convergence,slow convergence,this paper proposes an EP algorithm based on probe mutation.It can obtain a few mutational variables by reducing the dimensionality while fixating the others,and makes these individuals mutate which makes the individual always move forward to the direction with high fitness.By applying self-adaptive Gauss standard deviation to recover the search space,the individual has opportunity for jumping out the local optimum solutions.Experimental results show that the improved algorithm converges quickly,has high-quality solutions,and overcomes premature convergence problem

关 键 词：进化规划 探测变异 降维 数值优化 高斯变异 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]