JGBL paradigm : A novel strategy to enhance the exploration ability of NSGA-II

Ke LI; Sam KWONG; Kim-Fung MAN

doi:10.1145/2001858.2001915

JGBL paradigm : A novel strategy to enhance the exploration ability of NSGA-II

Ke LI, Sam KWONG, Kim-Fung MAN

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

1 Citation (Scopus)

Abstract

NSGA-II is one of the most efficient multi-objective evolutionary algorithms (MOEAs) for solving multi-objective optimization problems (MOPs). In this paper, a Jumping Gene Based Learning (JGBL) paradigm is proposed to enhance the exploration ability of NSGA-II. JGBL paradigm simulates the natural behavior of maize and is incorporated into the framework of the original NSGA-II. It only operates on the non-dominated solutions which are eliminated in the environmental selection procedure due to the low quality of crowded distance. The activation of JGBL operation is entirely adapted online according to the search status of evolutionary process to give a needed fuel when the population evolves slowly with inherent variation operators. © 2011 Authors.

Original language	English
Title of host publication	GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Editors	Natalio KRASNOGOR
Publisher	Association for Computing Machinery
Pages	99-100
Number of pages	2
ISBN (Print)	9781450306904
DOIs	https://doi.org/10.1145/2001858.2001915
Publication status	Published - Jul 2011
Externally published	Yes
Event	13th Annual Genetic and Evolutionary Computation Conference, GECCO'11 - Dublin, Ireland Duration: 12 Jul 2011 → 16 Jul 2011

Conference

Conference	13th Annual Genetic and Evolutionary Computation Conference, GECCO'11
Abbreviated title	GECCO'11
Country/Territory	Ireland
City	Dublin
Period	12/07/11 → 16/07/11

Keywords

jumping gene based learning
multi-objective optimization
nsga-ii

Access to Document

10.1145/2001858.2001915

Cite this

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title = "JGBL paradigm : A novel strategy to enhance the exploration ability of NSGA-II",

abstract = "NSGA-II is one of the most efficient multi-objective evolutionary algorithms (MOEAs) for solving multi-objective optimization problems (MOPs). In this paper, a Jumping Gene Based Learning (JGBL) paradigm is proposed to enhance the exploration ability of NSGA-II. JGBL paradigm simulates the natural behavior of maize and is incorporated into the framework of the original NSGA-II. It only operates on the non-dominated solutions which are eliminated in the environmental selection procedure due to the low quality of crowded distance. The activation of JGBL operation is entirely adapted online according to the search status of evolutionary process to give a needed fuel when the population evolves slowly with inherent variation operators. {\textcopyright} 2011 Authors.",

keywords = "jumping gene based learning, multi-objective optimization, nsga-ii",

author = "Ke LI and Sam KWONG and Kim-Fung MAN",

year = "2011",

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doi = "10.1145/2001858.2001915",

language = "English",

isbn = "9781450306904",

pages = "99--100",

editor = "Natalio KRASNOGOR",

booktitle = "GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation",

publisher = "Association for Computing Machinery",

note = "13th Annual Genetic and Evolutionary Computation Conference, GECCO'11, GECCO'11 ; Conference date: 12-07-2011 Through 16-07-2011",

}

LI, K, KWONG, S & MAN, K-F 2011, JGBL paradigm : A novel strategy to enhance the exploration ability of NSGA-II. in N KRASNOGOR (ed.), GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation. Association for Computing Machinery, pp. 99-100, 13th Annual Genetic and Evolutionary Computation Conference, GECCO'11, Dublin, Ireland, 12/07/11. https://doi.org/10.1145/2001858.2001915

JGBL paradigm : A novel strategy to enhance the exploration ability of NSGA-II. / LI, Ke; KWONG, Sam; MAN, Kim-Fung.
GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation. ed. / Natalio KRASNOGOR. Association for Computing Machinery, 2011. p. 99-100.

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

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AU - MAN, Kim-Fung

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AB - NSGA-II is one of the most efficient multi-objective evolutionary algorithms (MOEAs) for solving multi-objective optimization problems (MOPs). In this paper, a Jumping Gene Based Learning (JGBL) paradigm is proposed to enhance the exploration ability of NSGA-II. JGBL paradigm simulates the natural behavior of maize and is incorporated into the framework of the original NSGA-II. It only operates on the non-dominated solutions which are eliminated in the environmental selection procedure due to the low quality of crowded distance. The activation of JGBL operation is entirely adapted online according to the search status of evolutionary process to give a needed fuel when the population evolves slowly with inherent variation operators. © 2011 Authors.

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JGBL paradigm : A novel strategy to enhance the exploration ability of NSGA-II

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Keywords

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