File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: A grid-facilitated AIS-based network scheme for many-objective optimization

TitleA grid-facilitated AIS-based network scheme for many-objective optimization
Authors
KeywordsOptimization
Many-objective optimization
Artificial Immune Systems
Immune Network Theory
Issue Date2015
PublisherAssociation for Computing Machinery.
Citation
The 2015 Genetic and Evolutionary Computation Conference (GECCO 2015), Madrid, Spain, 11-15 July 2015. In Conference Proceedings, 2015, p. 1497-1498 How to Cite?
AbstractArtificial Immune Systems (AIS), one of the promising artificial intelligence methods, has been widely adopted in the optimization domain. However, their application to many-objective domain is rather scattered. In this respect, we extend the AIS-based algorithm to many-objective situations using the immune network theory facilitated by the grid technique. The network operations are employed not only for managing the diversity, but also to strengthen the exploitation and exploration pressure. The suppression-triggered activation and the archive-driven activation are both introduced in this study to exploit the promising region and to explore along the local Pareto-front. In addition, Grid technique is introduced to reduce the computation complexity in the identification process of the sensory range. Coupled with the grid-facilitated network scheme, the proposed algorithm improves the exploitation and exploration capability in many-objective optimization problem. © 2015 ACM, Inc.
DescriptionPoster Session: AIS-ACHEM
Persistent Identifierhttp://hdl.handle.net/10722/212211
ISBN

 

DC FieldValueLanguage
dc.contributor.authorTsang, WWP-
dc.contributor.authorLau, HYK-
dc.date.accessioned2015-07-21T02:27:47Z-
dc.date.available2015-07-21T02:27:47Z-
dc.date.issued2015-
dc.identifier.citationThe 2015 Genetic and Evolutionary Computation Conference (GECCO 2015), Madrid, Spain, 11-15 July 2015. In Conference Proceedings, 2015, p. 1497-1498-
dc.identifier.isbn978-1-4503-3488-4-
dc.identifier.urihttp://hdl.handle.net/10722/212211-
dc.descriptionPoster Session: AIS-ACHEM-
dc.description.abstractArtificial Immune Systems (AIS), one of the promising artificial intelligence methods, has been widely adopted in the optimization domain. However, their application to many-objective domain is rather scattered. In this respect, we extend the AIS-based algorithm to many-objective situations using the immune network theory facilitated by the grid technique. The network operations are employed not only for managing the diversity, but also to strengthen the exploitation and exploration pressure. The suppression-triggered activation and the archive-driven activation are both introduced in this study to exploit the promising region and to explore along the local Pareto-front. In addition, Grid technique is introduced to reduce the computation complexity in the identification process of the sensory range. Coupled with the grid-facilitated network scheme, the proposed algorithm improves the exploitation and exploration capability in many-objective optimization problem. © 2015 ACM, Inc.-
dc.languageeng-
dc.publisherAssociation for Computing Machinery.-
dc.relation.ispartofGECCO Companion '15: Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference-
dc.rightsGECCO Companion '15: Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference. Copyright © Association for Computing Machinery.-
dc.subjectOptimization-
dc.subjectMany-objective optimization-
dc.subjectArtificial Immune Systems-
dc.subjectImmune Network Theory-
dc.titleA grid-facilitated AIS-based network scheme for many-objective optimization-
dc.typeConference_Paper-
dc.identifier.emailLau, HYK: hyklau@hkucc.hku.hk-
dc.identifier.authorityLau, HYK=rp00137-
dc.description.naturelink_to_OA_fulltext-
dc.identifier.doi10.1145/2739482.2764715-
dc.identifier.hkuros245812-
dc.identifier.spage1497-
dc.identifier.epage1498-
dc.publisher.placeUnited States-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats