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Conference Paper: A New Asynchronous Parallel Evolutionary Algorithm for Function Optimization

TitleA New Asynchronous Parallel Evolutionary Algorithm for Function Optimization
Authors
Issue Date2002
PublisherSpringer-Verlag Berlin Heidelberg.
Citation
The 7th International Conference on Parallel Problem Solving from Nature (PPSN VII), Granada, Spain, 7-11 September 2002. In Merelo, JJ, Adamidis, P and Beyer, HG (Eds.). Parallel Problem Solving from Nature - PPSN VII, p. 401-410. Berlin, Heidelberg: Springer, 2002 How to Cite?
AbstractThis paper introduces a new asynchronous parallel evolutionary algorithm (APEA) based on the island model for solving function optimization problems. Our fully distributed APEA overlaps the communication and computation efficiently and is inherently fault-tolerant in a large-scale distributed computing environment. For the scalable BUMP problem, our APEA algorithm achieves the best solution for the 50-dimension problem, and is the first algorithm of which we are aware that can solve the 1,000,000- dimension problem. For other benchmark problems, our APEA finds the best solution to G7 in fewer time steps than [16][17], and finds a better solution to G10 than [17]. This work was partially supported by National Science Foundation (NFS) Instrumentation Grant EIA9911099.
Persistent Identifierhttp://hdl.handle.net/10722/93150
ISBN
ISSN
2005 Impact Factor: 0.302
2015 SCImago Journal Rankings: 0.252
Series/Report no.Lecture Notes in Computer Science

 

DC FieldValueLanguage
dc.contributor.authorLiu, Pen_HK
dc.contributor.authorLau, FCMen_HK
dc.contributor.authorLewis, MJen_HK
dc.contributor.authorWang, CLen_HK
dc.date.accessioned2010-09-25T14:52:24Z-
dc.date.available2010-09-25T14:52:24Z-
dc.date.issued2002en_HK
dc.identifier.citationThe 7th International Conference on Parallel Problem Solving from Nature (PPSN VII), Granada, Spain, 7-11 September 2002. In Merelo, JJ, Adamidis, P and Beyer, HG (Eds.). Parallel Problem Solving from Nature - PPSN VII, p. 401-410. Berlin, Heidelberg: Springer, 2002-
dc.identifier.isbn978-3-540-44139-7-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10722/93150-
dc.description.abstractThis paper introduces a new asynchronous parallel evolutionary algorithm (APEA) based on the island model for solving function optimization problems. Our fully distributed APEA overlaps the communication and computation efficiently and is inherently fault-tolerant in a large-scale distributed computing environment. For the scalable BUMP problem, our APEA algorithm achieves the best solution for the 50-dimension problem, and is the first algorithm of which we are aware that can solve the 1,000,000- dimension problem. For other benchmark problems, our APEA finds the best solution to G7 in fewer time steps than [16][17], and finds a better solution to G10 than [17]. This work was partially supported by National Science Foundation (NFS) Instrumentation Grant EIA9911099.-
dc.languageengen_HK
dc.publisherSpringer-Verlag Berlin Heidelberg.-
dc.relation.ispartofParallel Problem Solving from Nature - PPSN VIIen_HK
dc.relation.ispartofseriesLecture Notes in Computer Science-
dc.titleA New Asynchronous Parallel Evolutionary Algorithm for Function Optimizationen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailLau, FCM: fcmlau@cs.hku.hken_HK
dc.identifier.emailWang, CL: clwang@cs.hku.hken_HK
dc.identifier.authorityLau, FCM=rp00221en_HK
dc.identifier.authorityWang, CL=rp00183en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/3-540-45712-7_39-
dc.identifier.hkuros82158en_HK

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