Evolutionary programming using a mixed mutation strategy

Research output: Contribution to journalArticle

Standard

Evolutionary programming using a mixed mutation strategy. / Dong, H; He, Jun; Huang, H; Hou, W.

In: Information Sciences, Vol. 177, No. 1, 01.01.2007, p. 312-327.

Research output: Contribution to journalArticle

Harvard

APA

Vancouver

Author

Dong, H ; He, Jun ; Huang, H ; Hou, W. / Evolutionary programming using a mixed mutation strategy. In: Information Sciences. 2007 ; Vol. 177, No. 1. pp. 312-327.

Bibtex

@article{472d59fe359240339da4f155dbf00aba,
title = "Evolutionary programming using a mixed mutation strategy",
abstract = "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, Levy, 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. (c) 2006 Elsevier Inc. All rights reserved.",
keywords = "global optimization, design of algorithms, evolutionary programming, randomized algorithms, mixed strategy",
author = "H Dong and Jun He and H Huang and W Hou",
year = "2007",
month = jan,
day = "1",
doi = "10.1016/j.ins.2006.07.014",
language = "English",
volume = "177",
pages = "312--327",
journal = "Information Sciences",
issn = "0020-0255",
publisher = "Elsevier",
number = "1",

}

RIS

TY - JOUR

T1 - Evolutionary programming using a mixed mutation strategy

AU - Dong, H

AU - He, Jun

AU - Huang, H

AU - Hou, W

PY - 2007/1/1

Y1 - 2007/1/1

N2 - 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, Levy, 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. (c) 2006 Elsevier Inc. All rights reserved.

AB - 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, Levy, 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. (c) 2006 Elsevier Inc. All rights reserved.

KW - global optimization

KW - design of algorithms

KW - evolutionary programming

KW - randomized algorithms

KW - mixed strategy

U2 - 10.1016/j.ins.2006.07.014

DO - 10.1016/j.ins.2006.07.014

M3 - Article

VL - 177

SP - 312

EP - 327

JO - Information Sciences

JF - Information Sciences

SN - 0020-0255

IS - 1

ER -