Approximation model guided selection for evolutionary multiobjective optimization

Aimin Zhou; Qingfu Zhang; Guixu Zhang

doi:10.1007/978-3-642-37140-0_31

Approximation model guided selection for evolutionary multiobjective optimization

Aimin Zhou^*
, Qingfu Zhang
, Guixu Zhang

^*Corresponding author for this work

School of Computer Science and Technology

University of Essex

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

8 Scopus citations

Abstract

Selection plays a key role in a multiobjective evolutionary algorithm (MOEA). The dominance based selection operators or indicator based ones are widely used in most current MOEAs. This paper studies another kind of selection, in which a model is firstly built to approximate the Pareto front and then guides the selection of promising solutions into the next generation. Based on this idea, we propose two approximation model guided selection (AMS) operators in this paper: one uses a zero-order model to approximate the Pareto front, and the other uses a first-order model. The experimental results show that the new AMS operators performs well on some test instances.

Original language	English
Title of host publication	Evolutionary Multi-Criterion Optimization - 7th International Conference, EMO 2013, Proceedings
Pages	398-412
Number of pages	15
DOIs	https://doi.org/10.1007/978-3-642-37140-0_31
State	Published - 2013
Event	7th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2013 - Sheffield, United Kingdom Duration: 19 Mar 2013 → 22 Mar 2013

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	7811 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	7th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2013
Country/Territory	United Kingdom
City	Sheffield
Period	19/03/13 → 22/03/13

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10.1007/978-3-642-37140-0_31

Cite this

Zhou, A., Zhang, Q., & Zhang, G. (2013). Approximation model guided selection for evolutionary multiobjective optimization. In Evolutionary Multi-Criterion Optimization - 7th International Conference, EMO 2013, Proceedings (pp. 398-412). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7811 LNCS). https://doi.org/10.1007/978-3-642-37140-0_31

Zhou, Aimin ; Zhang, Qingfu ; Zhang, Guixu. / Approximation model guided selection for evolutionary multiobjective optimization. Evolutionary Multi-Criterion Optimization - 7th International Conference, EMO 2013, Proceedings. 2013. pp. 398-412 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{f7d9306755534c989f42a8413fff64af,

title = "Approximation model guided selection for evolutionary multiobjective optimization",

abstract = "Selection plays a key role in a multiobjective evolutionary algorithm (MOEA). The dominance based selection operators or indicator based ones are widely used in most current MOEAs. This paper studies another kind of selection, in which a model is firstly built to approximate the Pareto front and then guides the selection of promising solutions into the next generation. Based on this idea, we propose two approximation model guided selection (AMS) operators in this paper: one uses a zero-order model to approximate the Pareto front, and the other uses a first-order model. The experimental results show that the new AMS operators performs well on some test instances.",

author = "Aimin Zhou and Qingfu Zhang and Guixu Zhang",

year = "2013",

doi = "10.1007/978-3-642-37140-0\_31",

language = "英语",

isbn = "9783642371394",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "398--412",

booktitle = "Evolutionary Multi-Criterion Optimization - 7th International Conference, EMO 2013, Proceedings",

note = "7th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2013 ; Conference date: 19-03-2013 Through 22-03-2013",

}

Zhou, A, Zhang, Q & Zhang, G 2013, Approximation model guided selection for evolutionary multiobjective optimization. in Evolutionary Multi-Criterion Optimization - 7th International Conference, EMO 2013, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7811 LNCS, pp. 398-412, 7th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2013, Sheffield, United Kingdom, 19/03/13. https://doi.org/10.1007/978-3-642-37140-0_31

Approximation model guided selection for evolutionary multiobjective optimization. / Zhou, Aimin; Zhang, Qingfu; Zhang, Guixu.
Evolutionary Multi-Criterion Optimization - 7th International Conference, EMO 2013, Proceedings. 2013. p. 398-412 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7811 LNCS).

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

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AB - Selection plays a key role in a multiobjective evolutionary algorithm (MOEA). The dominance based selection operators or indicator based ones are widely used in most current MOEAs. This paper studies another kind of selection, in which a model is firstly built to approximate the Pareto front and then guides the selection of promising solutions into the next generation. Based on this idea, we propose two approximation model guided selection (AMS) operators in this paper: one uses a zero-order model to approximate the Pareto front, and the other uses a first-order model. The experimental results show that the new AMS operators performs well on some test instances.

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Zhou A, Zhang Q, Zhang G. Approximation model guided selection for evolutionary multiobjective optimization. In Evolutionary Multi-Criterion Optimization - 7th International Conference, EMO 2013, Proceedings. 2013. p. 398-412. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-37140-0_31

Approximation model guided selection for evolutionary multiobjective optimization

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