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

18 Citations (Scopus)

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. Copyright 2007 ACM.

Original language	English
Title of host publication	Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference
Pages	617-623
DOIs	https://doi.org/10.1145/1276958.1277082
Publication status	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

Conference

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

Research Keywords

Biased
Biased initialization
Estimation of distribution algorithm
Global optimization
Multiobjective optimization

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10.1145/1276958.1277082

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@inproceedings{ed07c20de53541409664e2c6f1c91703,

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. Copyright 2007 ACM.",

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 = "English",

isbn = "1595936971",

pages = "617--623",

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

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. 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.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › 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. Copyright 2007 ACM.

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. Copyright 2007 ACM.

KW - Biased

KW - Biased initialization

KW - Estimation of distribution algorithm

KW - Global optimization

KW - Multiobjective optimization

UR - http://www.scopus.com/inward/record.url?scp=34548078158&partnerID=8YFLogxK

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-34548078158&origin=recordpage

U2 - 10.1145/1276958.1277082

DO - 10.1145/1276958.1277082

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 1595936971

SN - 9781595936974

SP - 617

EP - 623

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

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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