关键词: 演化规划算法, 启发式变异, 连续优化, 收敛速度

Abstract:

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.

Key words: evolutionary programming algorithm, heuristic mutation, continuous optimization, convergence speed