Efficient Multi-Objective Optimization using 2-Population Cooperative Coevolution

Ciprian Zavoianu; Edwin Lughofer; Wolfgang Amrhein; Erich Klement

doi:10.1007/978-3-642-53856-8-32

Efficient Multi-Objective Optimization using 2-Population Cooperative Coevolution

Ciprian Zavoianu
, Edwin Lughofer
, Wolfgang Amrhein
, Erich Klement

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

Abstract

We propose a 2-population cooperative coevolutionary optimization method that can efficiently solve multi-objective optimization problems as it successfully combines positive traits from classic multi-objective evolutionary algorithms and from newer optimization approaches that explore the concept of differential evolution. A key part of the algorithm lies in the proposed dual fitness sharing mechanism that is able to smoothly transfer information between the two coevolved populations without negatively impacting the independent evolutionary process behavior that characterizes each population.

Original language	English
Title of host publication	Computer Aided Systems Theory - EUROCAST 2013
Editors	Roberto Moreno-Díaz, Franz Pichler, Alexis Quesada-Arencibia
Publisher	Springer Berlin Heidelberg
Pages	251-258
Number of pages	8
Volume	8111
ISBN (Print)	978-3-642-53855-1
DOIs	https://doi.org/10.1007/978-3-642-53856-8-32
Publication status	Published - 2013

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	8111 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Fields of science

101001 Algebra
101 Mathematics
102 Computer Sciences
101013 Mathematical logic
101020 Technical mathematics
102001 Artificial intelligence
102003 Image processing
202027 Mechatronics
101019 Stochastics
211913 Quality assurance

JKU Focus areas

Computation in Informatics and Mathematics
Mechatronics and Information Processing

Access to Document

10.1007/978-3-642-53856-8-32

Cite this

Zavoianu, C., Lughofer, E., Amrhein, W., & Klement, E. (2013). Efficient Multi-Objective Optimization using 2-Population Cooperative Coevolution. In Roberto Moreno-Díaz, Franz Pichler, Alexis Quesada-Arencibia (Ed.), Computer Aided Systems Theory - EUROCAST 2013 (Vol. 8111, pp. 251-258). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8111 LNCS, No. PART 1). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-53856-8-32

Zavoianu, Ciprian ; Lughofer, Edwin ; Amrhein, Wolfgang et al. / Efficient Multi-Objective Optimization using 2-Population Cooperative Coevolution. Computer Aided Systems Theory - EUROCAST 2013. editor / Roberto Moreno-Díaz, Franz Pichler, Alexis Quesada-Arencibia. Vol. 8111 Springer Berlin Heidelberg, 2013. pp. 251-258 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

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title = "Efficient Multi-Objective Optimization using 2-Population Cooperative Coevolution",

abstract = "We propose a 2-population cooperative coevolutionary optimization method that can efficiently solve multi-objective optimization problems as it successfully combines positive traits from classic multi-objective evolutionary algorithms and from newer optimization approaches that explore the concept of differential evolution. A key part of the algorithm lies in the proposed dual fitness sharing mechanism that is able to smoothly transfer information between the two coevolved populations without negatively impacting the independent evolutionary process behavior that characterizes each population.",

author = "Ciprian Zavoianu and Edwin Lughofer and Wolfgang Amrhein and Erich Klement",

year = "2013",

doi = "10.1007/978-3-642-53856-8-32",

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isbn = "978-3-642-53855-1",

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

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Zavoianu, C, Lughofer, E, Amrhein, W & Klement, E 2013, Efficient Multi-Objective Optimization using 2-Population Cooperative Coevolution. in Roberto Moreno-Díaz, Franz Pichler, Alexis Quesada-Arencibia (ed.), Computer Aided Systems Theory - EUROCAST 2013. vol. 8111, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 8111 LNCS, Springer Berlin Heidelberg, pp. 251-258. https://doi.org/10.1007/978-3-642-53856-8-32

Efficient Multi-Objective Optimization using 2-Population Cooperative Coevolution. / Zavoianu, Ciprian; Lughofer, Edwin; Amrhein, Wolfgang et al.
Computer Aided Systems Theory - EUROCAST 2013. ed. / Roberto Moreno-Díaz, Franz Pichler, Alexis Quesada-Arencibia. Vol. 8111 Springer Berlin Heidelberg, 2013. p. 251-258 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8111 LNCS, No. PART 1).

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

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AB - We propose a 2-population cooperative coevolutionary optimization method that can efficiently solve multi-objective optimization problems as it successfully combines positive traits from classic multi-objective evolutionary algorithms and from newer optimization approaches that explore the concept of differential evolution. A key part of the algorithm lies in the proposed dual fitness sharing mechanism that is able to smoothly transfer information between the two coevolved populations without negatively impacting the independent evolutionary process behavior that characterizes each population.

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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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Zavoianu C, Lughofer E, Amrhein W , Klement E. Efficient Multi-Objective Optimization using 2-Population Cooperative Coevolution. In Roberto Moreno-Díaz, Franz Pichler, Alexis Quesada-Arencibia, editor, Computer Aided Systems Theory - EUROCAST 2013. Vol. 8111. Springer Berlin Heidelberg. 2013. p. 251-258. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-642-53856-8-32

Efficient Multi-Objective Optimization using 2-Population Cooperative Coevolution

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