Global multiobjective optimization via estimation of distribution algorithm with biased initialization and crossover

Aimin Zhou; Qingfu Zhang; Yaochu Jin; Bernhard Sendhoff; Edward Tsang

doi:10.1145/1276958.1277082

Global multiobjective optimization via estimation of distribution algorithm with biased initialization and crossover

Aimin Zhou, Qingfu Zhang, Yaochu Jin, Bernhard Sendhoff, Edward Tsang

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

18 Scopus citations

Abstract

Multiobjective optimization problems with many local Pareto fronts is a big challenge to evolutionary algorithms. In this paper, two operators, biased initialization and biased crossover, are proposed to improve the global search ability of RM-MEDA, a recently proposed multiobjective estimation of distribution algorithm. Biased initialization inserts several globally Pareto optimal solutions into the initial population; biased crossover combines the location information of some best solutions found so far and globally statistical information extracted from current population. Experiments have been conducted to study the effects of these two operators.

Original language	English
Title of host publication	Proceedings of GECCO 2007
Subtitle of host publication	Genetic and Evolutionary Computation Conference
Pages	617-623
Number of pages	7
DOIs	https://doi.org/10.1145/1276958.1277082
State	Published - 2007
Externally published	Yes
Event	9th Annual Genetic and Evolutionary Computation Conference, GECCO 2007 - London, United Kingdom Duration: 7 Jul 2007 → 11 Jul 2007

Publication series

Name	Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference

Conference

Conference	9th Annual Genetic and Evolutionary Computation Conference, GECCO 2007
Country/Territory	United Kingdom
City	London
Period	7/07/07 → 11/07/07

Keywords

Biased
Biased initialization
Estimation of distribution algorithm
Global optimization
Multiobjective optimization

Access to Document

10.1145/1276958.1277082

Cite this

Zhou, A., Zhang, Q., Jin, Y., Sendhoff, B., & Tsang, E. (2007). Global multiobjective optimization via estimation of distribution algorithm with biased initialization and crossover. In Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference (pp. 617-623). (Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference). https://doi.org/10.1145/1276958.1277082

@inproceedings{9e9f6c24b8e9447c938c1bf365204b35,

title = "Global multiobjective optimization via estimation of distribution algorithm with biased initialization and crossover",

abstract = "Multiobjective optimization problems with many local Pareto fronts is a big challenge to evolutionary algorithms. In this paper, two operators, biased initialization and biased crossover, are proposed to improve the global search ability of RM-MEDA, a recently proposed multiobjective estimation of distribution algorithm. Biased initialization inserts several globally Pareto optimal solutions into the initial population; biased crossover combines the location information of some best solutions found so far and globally statistical information extracted from current population. Experiments have been conducted to study the effects of these two operators.",

keywords = "Biased, Biased initialization, Estimation of distribution algorithm, Global optimization, Multiobjective optimization",

author = "Aimin Zhou and Qingfu Zhang and Yaochu Jin and Bernhard Sendhoff and Edward Tsang",

year = "2007",

doi = "10.1145/1276958.1277082",

language = "英语",

isbn = "1595936971",

series = "Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference",

pages = "617--623",

booktitle = "Proceedings of GECCO 2007",

note = "9th Annual Genetic and Evolutionary Computation Conference, GECCO 2007 ; Conference date: 07-07-2007 Through 11-07-2007",

}

Zhou, A, Zhang, Q, Jin, Y, Sendhoff, B & Tsang, E 2007, Global multiobjective optimization via estimation of distribution algorithm with biased initialization and crossover. in Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference. Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference, pp. 617-623, 9th Annual Genetic and Evolutionary Computation Conference, GECCO 2007, London, United Kingdom, 7/07/07. https://doi.org/10.1145/1276958.1277082

Global multiobjective optimization via estimation of distribution algorithm with biased initialization and crossover. / Zhou, Aimin; Zhang, Qingfu; Jin, Yaochu et al.
Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference. 2007. p. 617-623 (Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference).

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

TY - GEN

T1 - Global multiobjective optimization via estimation of distribution algorithm with biased initialization and crossover

AU - Zhou, Aimin

AU - Zhang, Qingfu

AU - Jin, Yaochu

AU - Sendhoff, Bernhard

AU - Tsang, Edward

PY - 2007

Y1 - 2007

N2 - Multiobjective optimization problems with many local Pareto fronts is a big challenge to evolutionary algorithms. In this paper, two operators, biased initialization and biased crossover, are proposed to improve the global search ability of RM-MEDA, a recently proposed multiobjective estimation of distribution algorithm. Biased initialization inserts several globally Pareto optimal solutions into the initial population; biased crossover combines the location information of some best solutions found so far and globally statistical information extracted from current population. Experiments have been conducted to study the effects of these two operators.

AB - Multiobjective optimization problems with many local Pareto fronts is a big challenge to evolutionary algorithms. In this paper, two operators, biased initialization and biased crossover, are proposed to improve the global search ability of RM-MEDA, a recently proposed multiobjective estimation of distribution algorithm. Biased initialization inserts several globally Pareto optimal solutions into the initial population; biased crossover combines the location information of some best solutions found so far and globally statistical information extracted from current population. Experiments have been conducted to study the effects of these two operators.

KW - Biased

KW - Biased initialization

KW - Estimation of distribution algorithm

KW - Global optimization

KW - Multiobjective optimization

UR - https://www.scopus.com/pages/publications/34548078158

U2 - 10.1145/1276958.1277082

DO - 10.1145/1276958.1277082

M3 - 会议稿件

AN - SCOPUS:34548078158

SN - 1595936971

SN - 9781595936974

T3 - Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference

SP - 617

EP - 623

BT - Proceedings of GECCO 2007

T2 - 9th Annual Genetic and Evolutionary Computation Conference, GECCO 2007

Y2 - 7 July 2007 through 11 July 2007

ER -

Global multiobjective optimization via estimation of distribution algorithm with biased initialization and crossover

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Keywords

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