常用的演化规划算法( EP) 的变异是基于固定的概率分布，鲁棒性较差．文中分析了变异算子对演化规划算法计算效率的影响，指出了Gauss 变异、Cauchy 变异和Lévy变异算子缺少启发式信息的不足，并据此设计了一种根据种群个体差异信息的启发式变异算子，用算子抽取的个体差异来更新变异步长，允许个体有机会在某些维数保持原状，只是进行部分维数上的变异．启发式变异算子能使演化规划算法更好地适应不同特点的连续优化问题，从总体上增强算法的鲁棒性．在求解多个Benchmark 测试问题的数值实验中，基于启发式变异的改进演化规划算法比当前6 种等概率分布演化规划算法有更快的收敛速度和更优的平均性能．

The common evolutionary programming ( EP) algorithms are of poor robustness because they perform themutation based on a fixed probability distribution.In this paper,first,the influence of mutation operators on thecomputational efficiency of evolutionary programming algorithms is analyzed,and the essential drawback of Gauss,Cauchy and Lévy mutation operators,namely the lack of heuristic information,is pointed out.Then,a heuristicmutation operator based on the differential information among individuals is designed,which uses the difference betweentwo individuals to update the mutated variables and to provide chances for an individual to maintain its statusquo in some dimensions.With the help of the proposed heuristic mutation operator,evolutionary programming algorithmscan adapt to different continuous optimization problems and the algorithm robustness improves.Numericalexperiments of several Benchmark problems demonstrate that the improved evolutionary programming algorithmbased on heuristic mutation is of higher convergence speed and better average performance than six other evolutionaryalgorithms based on probability distribution.