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

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

8 Citations (Scopus)

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. © 2013 Springer-Verlag.

Original language	English
Title of host publication	Evolutionary Multi-Criterion Optimization
Subtitle of host publication	7th International Conference, EMO 2013, Proceedings
Publisher	Springer Verlag
Pages	398-412
Volume	7811 LNCS
ISBN (Print)	9783642371394
DOIs	https://doi.org/10.1007/978-3-642-37140-0_31
Publication status	Published - 2013
Externally published	Yes
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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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 (Vol. 7811 LNCS, pp. 398-412). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7811 LNCS). Springer Verlag. 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. Vol. 7811 LNCS Springer Verlag, 2013. pp. 398-412 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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. {\textcopyright} 2013 Springer-Verlag.",

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

year = "2013",

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

language = "English",

isbn = "9783642371394",

volume = "7811 LNCS",

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

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pages = "398--412",

booktitle = "Evolutionary Multi-Criterion Optimization",

address = "Germany",

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

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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. vol. 7811 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7811 LNCS, Springer Verlag, 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. Vol. 7811 LNCS Springer Verlag, 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: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

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AU - Zhang, Qingfu

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N2 - 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. © 2013 Springer-Verlag.

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. © 2013 Springer-Verlag.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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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. Vol. 7811 LNCS. Springer Verlag. 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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