Combining mutation operators in evolutionary programming

Chellapilla, Kumar

doi:10.1109/4235.735431

Cited by 216 publications

(88 citation statements)

References 12 publications

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“…O operador de mutação, com distribuição de Cauchy [22],éútil para escapar deótimos locais, enquanto o operador lognormal providencia a convergência local mais rápida em funções convexas. A estratégia de mutação, combinando estes dois operadores de mutação, pode explorar as propriedades desejadas de convergência da PE.…”

Section: Figura 32: Diferença Entre As Funções Densidade De Cauchy Eunclassified

Fundamentos, Potencialidades e Aplicações de Algoritmos Evolutivos

2003

Notas Em Matemática Aplicada

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Section: Figura 32: Diferença Entre As Funções Densidade De Cauchy Eunclassified

Fundamentos, Potencialidades e Aplicações de Algoritmos Evolutivos

2003

Notas Em Matemática Aplicada

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“…The rest of the algorithm is exactly the same as FEP and CEP. Chellapilla [19] has recently presented some more results on comparing different mutation operators in EP.…”

Section: An Improved Fast Evolutionary Programmingmentioning

confidence: 99%

Evolutionary programming made faster

Yao

Liu

Lin

1999

IEEE Trans. Evol. Computat.

3,216

150

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Abstract-Evolutionary programming (EP) has been applied with success to many numerical and combinatorial optimization problems in recent years. EP has rather slow convergence rates, however, on some function optimization problems. In this paper, a "fast EP" (FEP) is proposed which uses a Cauchy instead of Gaussian mutation as the primary search operator. The relationship between FEP and classical EP (CEP) is similar to that between fast simulated annealing and the classical version. Both analytical and empirical studies have been carried out to evaluate the performance of FEP and CEP for different function optimization problems. This paper shows that FEP is very good at search in a large neighborhood while CEP is better at search in a small local neighborhood. For a suite of 23 benchmark problems, FEP performs much better than CEP for multimodal functions with many local minima while being comparable to CEP in performance for unimodal and multimodal functions with only a few local minima. This paper also shows the relationship between the search step size and the probability of finding a global optimum and thus explains why FEP performs better than CEP on some functions but not on others. In addition, the importance of the neighborhood size and its relationship to the probability of finding a near-optimum is investigated. Based on these analyses, an improved FEP (IFEP) is proposed and tested empirically. This technique mixes different search operators (mutations). The experimental results show that IFEP performs better than or as well as the better of FEP and CEP for most benchmark problems tested.Index Terms-Cauchy mutations, evolutionary programming, mixing operators.

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“…• Evolutionary programming with adaptive Lévy mutations (ALEP) [Lee and Yao 2004] • Attractive and repulsive particle swarm optimization (arPSO) [Vesterstroem and Thomsen 2004] • Cooperative co-evolutionary genetic algorithm (CCGA) [Bergh and Englebrecht 2004] • Classical evolutionary programming (CEP) [Yao et al 1999] • Conventional evolutionary programming with adaptive mutations (CEP/AM) [Chellapilla 1998]…”

Section: Why Differential Evolution?mentioning

confidence: 99%