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

Publikation: Beitrag in Buch/Bericht/Konferenzband › Konferenzbeitrag › Begutachtung

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.

Originalsprache	Englisch
Titel	Computer Aided Systems Theory - EUROCAST 2013
Herausgeber*innen	Roberto Moreno-Díaz, Franz Pichler, Alexis Quesada-Arencibia
Verlag	Springer Berlin Heidelberg
Seiten	251-258
Seitenumfang	8
Band	8111
ISBN (Print)	978-3-642-53855-1
DOIs	https://doi.org/10.1007/978-3-642-53856-8-32
Publikationsstatus	Veröffentlicht - 2013

Publikationsreihe

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

Wissenschaftszweige

101001 Algebra
101 Mathematik
102 Informatik
101013 Mathematische Logik
101020 Technische Mathematik
102001 Artificial Intelligence
102003 Bildverarbeitung
202027 Mechatronik
101019 Stochastik
211913 Qualitätssicherung

JKU-Schwerpunkte

Computation in Informatics and Mathematics
Mechatronics and Information Processing

Zugriff auf Dokument

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

Andere Dateien und Links

Verknüpfung zur Publikation in Scopus

Dieses zitieren

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 (Hrsg.), Computer Aided Systems Theory - EUROCAST 2013 (Band 8111, S. 251-258). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 8111 LNCS, Nr. 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. Hrsg. / Roberto Moreno-Díaz, Franz Pichler, Alexis Quesada-Arencibia. Band 8111 Springer Berlin Heidelberg, 2013. S. 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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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 (Hrsg.), Computer Aided Systems Theory - EUROCAST 2013. Bd. 8111, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Nr. PART 1, Bd. 8111 LNCS, Springer Berlin Heidelberg, S. 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. Hrsg. / Roberto Moreno-Díaz, Franz Pichler, Alexis Quesada-Arencibia. Band 8111 Springer Berlin Heidelberg, 2013. S. 251-258 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 8111 LNCS, Nr. PART 1).

Publikation: Beitrag in Buch/Bericht/Konferenzband › Konferenzbeitrag › Begutachtung

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T1 - Efficient Multi-Objective Optimization using 2-Population Cooperative Coevolution

AU - Zavoianu, Ciprian

AU - Lughofer, Edwin

AU - Amrhein, Wolfgang

AU - Klement, Erich

PY - 2013

Y1 - 2013

N2 - 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.

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.

UR - https://www.scopus.com/pages/publications/84892591386

U2 - 10.1007/978-3-642-53856-8-32

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

M3 - Conference proceedings

SN - 978-3-642-53855-1

VL - 8111

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

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BT - Computer Aided Systems Theory - EUROCAST 2013

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PB - Springer Berlin Heidelberg

ER -

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, Hrsg., Computer Aided Systems Theory - EUROCAST 2013. Band 8111. Springer Berlin Heidelberg. 2013. S. 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