A novel search biases selection strategy for constrained evolutionary optimization

Zhang Min; Geng Huantong; Luo Wenjian; Huang Linfeng; Wang Xufa

A novel search biases selection strategy for constrained evolutionary optimization

Zhang Min^*
, Geng Huantong
, Luo Wenjian
, Huang Linfeng
, Wang Xufa

^*Corresponding author for this work

University of Science and Technology of China

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

The issues of the search biases selection based on stochastic ranking are pointed out by an example with three possible outputs and are also demonstrated by an experiment designed here. In order to improve the explicit search biases ability in feasible regions, three conditions for explicit search biases are presented and a novel search biases selection strategy with stochastic ranking is proposed in this paper. This strategy is applied to our new algorithm based on ES (Evolution Strategy). The new algorithm has been tested on 13 common benchmark functions and the experimental results have demonstrated that to some extent the convergence speed, the numerical accuracy and stability of best solutions are improved.

Original language	English
Title of host publication	2006 IEEE Congress on Evolutionary Computation, CEC 2006
Pages	1845-1850
Number of pages	6
State	Published - 2006
Externally published	Yes
Event	2006 IEEE Congress on Evolutionary Computation, CEC 2006 - Vancouver, BC, Canada Duration: 16 Jul 2006 → 21 Jul 2006

Publication series

Name	2006 IEEE Congress on Evolutionary Computation, CEC 2006

Conference

Conference	2006 IEEE Congress on Evolutionary Computation, CEC 2006
Country/Territory	Canada
City	Vancouver, BC
Period	16/07/06 → 21/07/06

Cite this

@inproceedings{bbb1dbc9bd584104b2a889c4491da3ec,

title = "A novel search biases selection strategy for constrained evolutionary optimization",

abstract = "The issues of the search biases selection based on stochastic ranking are pointed out by an example with three possible outputs and are also demonstrated by an experiment designed here. In order to improve the explicit search biases ability in feasible regions, three conditions for explicit search biases are presented and a novel search biases selection strategy with stochastic ranking is proposed in this paper. This strategy is applied to our new algorithm based on ES (Evolution Strategy). The new algorithm has been tested on 13 common benchmark functions and the experimental results have demonstrated that to some extent the convergence speed, the numerical accuracy and stability of best solutions are improved.",

author = "Zhang Min and Geng Huantong and Luo Wenjian and Huang Linfeng and Wang Xufa",

year = "2006",

language = "英语",

isbn = "0780394879",

series = "2006 IEEE Congress on Evolutionary Computation, CEC 2006",

pages = "1845--1850",

booktitle = "2006 IEEE Congress on Evolutionary Computation, CEC 2006",

note = "2006 IEEE Congress on Evolutionary Computation, CEC 2006 ; Conference date: 16-07-2006 Through 21-07-2006",

}

Min, Z, Huantong, G, Wenjian, L, Linfeng, H & Xufa, W 2006, A novel search biases selection strategy for constrained evolutionary optimization. in 2006 IEEE Congress on Evolutionary Computation, CEC 2006., 1688531, 2006 IEEE Congress on Evolutionary Computation, CEC 2006, pp. 1845-1850, 2006 IEEE Congress on Evolutionary Computation, CEC 2006, Vancouver, BC, Canada, 16/07/06.

A novel search biases selection strategy for constrained evolutionary optimization. / Min, Zhang; Huantong, Geng; Wenjian, Luo et al.
2006 IEEE Congress on Evolutionary Computation, CEC 2006. 2006. p. 1845-1850 1688531 (2006 IEEE Congress on Evolutionary Computation, CEC 2006).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Huantong, Geng

AU - Wenjian, Luo

AU - Linfeng, Huang

AU - Xufa, Wang

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Y1 - 2006

N2 - The issues of the search biases selection based on stochastic ranking are pointed out by an example with three possible outputs and are also demonstrated by an experiment designed here. In order to improve the explicit search biases ability in feasible regions, three conditions for explicit search biases are presented and a novel search biases selection strategy with stochastic ranking is proposed in this paper. This strategy is applied to our new algorithm based on ES (Evolution Strategy). The new algorithm has been tested on 13 common benchmark functions and the experimental results have demonstrated that to some extent the convergence speed, the numerical accuracy and stability of best solutions are improved.

AB - The issues of the search biases selection based on stochastic ranking are pointed out by an example with three possible outputs and are also demonstrated by an experiment designed here. In order to improve the explicit search biases ability in feasible regions, three conditions for explicit search biases are presented and a novel search biases selection strategy with stochastic ranking is proposed in this paper. This strategy is applied to our new algorithm based on ES (Evolution Strategy). The new algorithm has been tested on 13 common benchmark functions and the experimental results have demonstrated that to some extent the convergence speed, the numerical accuracy and stability of best solutions are improved.

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M3 - 会议稿件

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SN - 0780394879

SN - 9780780394872

T3 - 2006 IEEE Congress on Evolutionary Computation, CEC 2006

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BT - 2006 IEEE Congress on Evolutionary Computation, CEC 2006

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Y2 - 16 July 2006 through 21 July 2006

ER -

A novel search biases selection strategy for constrained evolutionary optimization

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