A single-point mutation evolutionary programming

Ji, Mingjun; Tang, Huanwen; Guo, Jing

doi:10.1016/j.ipl.2004.03.002

Cited by 25 publications

(18 citation statements)

References 19 publications

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“…A total of 22 test functions are chosen from references [17], [12] and their definitions are given in the appendix. These functions span a range of complexity from unimodal to multimodal, and from low to high dimensionality.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…The goal of the second experiment is to compare MSEP to CEP, FEP, LEP and SPMEP for another set of test functions from the literature [12]. While the function definitions are identical, the choice of parameters in this reference is different from that in the first experiment.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…In practice, it is difficult to determine the optimal value of β for a given problem. The Single-Point Mutation Evolutionary Programming (SPMEP) algorithm searches for only one component of the solution in each generation, and its deviation follows a fixed mutation scheme [12]. SPMEP is superior to both CEP and FEP for many multimodal and high-dimensional functions, provided that they can be expressed as a sum of one-dimensional functions.…”

Section: Introductionmentioning

confidence: 99%

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Evolutionary programming using a mixed mutation strategy

Dong

Huang

et al. 2007

Information Sciences

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Different mutation operators have been proposed in evolutionary programming, but for each operator there are some types of optimization problems that cannot be solved efficiently. A mixed strategy, integrating several mutation operators into a single algorithm, can overcome this problem. Inspired by evolutionary game theory, this paper presents a mixed strategy evolutionary programming algorithm that employs the Gaussian, Cauchy, Lévy, and single-point mutation operators. The novel algorithm is tested on a set of 22 benchmark problems. The results show that the mixed strategy performs equally well or better than the best of the four pure strategies does, for all of the benchmark problems.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Experiments and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evolutionary programming using a mixed mutation strategy

Dong

Huang

et al. 2007

Information Sciences

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“…As adaptability improves the performance of evolutionary programming, other adaptive mutation operators have been proposed [8,10]. Lee et al [10] employed the difference of fitness function between a parent and its immediate offspring as an indication to adjust the deviation parameters in different generations.…”

Section: Introductionmentioning

confidence: 99%

“…This helps to adjust an individual's mutation step length. Single-Point Mutation Evolutionary Programming (SPMEP) [8] is another adaptive mutation EP algorithm. Unlike CEP, it searches only one component of a solution in each generation and its deviation follows a fixed mutation scheme.…”

Section: Introductionmentioning

confidence: 99%