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Conference Paper: Clustering-based multi-objective immune optimization evolutionary algorithm

TitleClustering-based multi-objective immune optimization evolutionary algorithm
Authors
KeywordsArtificial immune systems
Evolutionary algorithm
Multi-objective optimization
Evolution process
Immune optimization
Issue Date2012
PublisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/
Citation
The 11th International Conference on Artificial Immune Systems (ICARIS 2012), Taormina, Italy, 28-31 August 2012. In Lecture Notes in Computer Science, 2012, v. 7597, p. 72-85 How to Cite?
AbstractIn everyday life, there are plentiful cases that we need to find good solutions such that risk, cost and many other factors are to be optimized. These problems are typical examples of multi-objective optimization problems. Evolutionary algorithms are often employed for solving it. Due to the characteristics of learning and adaptability, self-organization and memory capabilities, one of the biological inspired AI methods - artificial immune systems (AIS) is considered to be a class of evolutionary techniques that can be deployed for solving this problem. This paper aims to propose a new AIS-based framework focusing on distributed and self-organization characteristics. Population of solutions is decomposed into sub-populations forming clusters. Sub-populations in each cluster undergo independent evolution processes. These clusters are then combined and re-decomposed. The proposed mechanism aims to reduce the complexity in the evolution processes, enhance the exploitation ability and achieve quick convergence. It is evaluated and compared with representative algorithms. © 2012 Springer-Verlag.
DescriptionLNCS v. 7597 has title: Artificial Immune Systems: 11th International Conference, ICARIS 2012 ... proceedings
Persistent Identifierhttp://hdl.handle.net/10722/165333
ISBN
ISSN
2005 Impact Factor: 0.402
2015 SCImago Journal Rankings: 0.252

 

DC FieldValueLanguage
dc.contributor.authorTsang, WWPen_US
dc.contributor.authorLau, HYKen_US
dc.date.accessioned2012-09-20T08:17:16Z-
dc.date.available2012-09-20T08:17:16Z-
dc.date.issued2012en_US
dc.identifier.citationThe 11th International Conference on Artificial Immune Systems (ICARIS 2012), Taormina, Italy, 28-31 August 2012. In Lecture Notes in Computer Science, 2012, v. 7597, p. 72-85en_US
dc.identifier.isbn978-3-642-33756-7-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10722/165333-
dc.descriptionLNCS v. 7597 has title: Artificial Immune Systems: 11th International Conference, ICARIS 2012 ... proceedings-
dc.description.abstractIn everyday life, there are plentiful cases that we need to find good solutions such that risk, cost and many other factors are to be optimized. These problems are typical examples of multi-objective optimization problems. Evolutionary algorithms are often employed for solving it. Due to the characteristics of learning and adaptability, self-organization and memory capabilities, one of the biological inspired AI methods - artificial immune systems (AIS) is considered to be a class of evolutionary techniques that can be deployed for solving this problem. This paper aims to propose a new AIS-based framework focusing on distributed and self-organization characteristics. Population of solutions is decomposed into sub-populations forming clusters. Sub-populations in each cluster undergo independent evolution processes. These clusters are then combined and re-decomposed. The proposed mechanism aims to reduce the complexity in the evolution processes, enhance the exploitation ability and achieve quick convergence. It is evaluated and compared with representative algorithms. © 2012 Springer-Verlag.-
dc.languageengen_US
dc.publisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/-
dc.relation.ispartofLecture Notes in Computer Scienceen_US
dc.rightsThe original publication is available at www.springerlink.com-
dc.subjectArtificial immune systems-
dc.subjectEvolutionary algorithm-
dc.subjectMulti-objective optimization-
dc.subjectEvolution process-
dc.subjectImmune optimization-
dc.titleClustering-based multi-objective immune optimization evolutionary algorithmen_US
dc.typeConference_Paperen_US
dc.identifier.emailTsang, WWP: h0246582@hku.hken_US
dc.identifier.emailLau, HYK: hyklau@hkucc.hku.hk-
dc.identifier.authorityLau, HYK=rp00137en_US
dc.identifier.doi10.1007/978-3-642-33757-4_6-
dc.identifier.scopuseid_2-s2.0-84866396800-
dc.identifier.hkuros209415en_US
dc.identifier.volume7597-
dc.identifier.spage72-
dc.identifier.epage85-
dc.publisher.placeGermany-
dc.customcontrol.immutablesml 130515-

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