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Article: Function optimization by using genetic algorithms with individuals having different birth and survival rates

TitleFunction optimization by using genetic algorithms with individuals having different birth and survival rates
Authors
KeywordsFunction optimization
Genetic algorithms
Genetic operators
Issue Date2001
PublisherTaylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/0305215x.asp
Citation
Engineering Optimization, 2001, v. 33 n. 6, p. 749-777 How to Cite?
AbstractThis paper proposes an effective approach to function optimisation using the concept of genetic algorithms. The proposed approach differs from the canonical genetic algorithm in that the populations of candidate solutions consist of individuals from various age-groups, and each individual is incorporated with an age attribute to enable its birth and survival rates to be governed by predefined aging patterns. In order to ensure a stable search process, the condition that governs the relationships among the various birth and survival rates is determined. By generating the evolution of the populations with the genetic operators of selection, crossover and mutation, the proposed approach can provide excellent results by maintaining a better balance between exploitation and exploration of the solution space. A thorough study on the effects of the genetic parameters is carried out to examine the convergence behaviour of the proposed approach, and the findings illustrate how the convergence rate and the solution's quality are affected by the changes in the genetic parameters. The results of applying the proposed approach to solve five benchmark test problems are compared with those obtained by using the canonical genetic algorithm. Indeed, the proposed approach's performance is shown to surpass those of the canonical genetic algorithm. © 2001 OPA (Overseas Publishers Association) N.V. Published by license under the Gordon and Breach Science Publishers imprint, a member of the Taylor & Francis Group.
Persistent Identifierhttp://hdl.handle.net/10722/74268
ISSN
2015 Impact Factor: 1.38
2015 SCImago Journal Rankings: 0.866
References

 

DC FieldValueLanguage
dc.contributor.authorMak, KLen_HK
dc.contributor.authorWong, YSen_HK
dc.date.accessioned2010-09-06T06:59:37Z-
dc.date.available2010-09-06T06:59:37Z-
dc.date.issued2001en_HK
dc.identifier.citationEngineering Optimization, 2001, v. 33 n. 6, p. 749-777en_HK
dc.identifier.issn0305-215Xen_HK
dc.identifier.urihttp://hdl.handle.net/10722/74268-
dc.description.abstractThis paper proposes an effective approach to function optimisation using the concept of genetic algorithms. The proposed approach differs from the canonical genetic algorithm in that the populations of candidate solutions consist of individuals from various age-groups, and each individual is incorporated with an age attribute to enable its birth and survival rates to be governed by predefined aging patterns. In order to ensure a stable search process, the condition that governs the relationships among the various birth and survival rates is determined. By generating the evolution of the populations with the genetic operators of selection, crossover and mutation, the proposed approach can provide excellent results by maintaining a better balance between exploitation and exploration of the solution space. A thorough study on the effects of the genetic parameters is carried out to examine the convergence behaviour of the proposed approach, and the findings illustrate how the convergence rate and the solution's quality are affected by the changes in the genetic parameters. The results of applying the proposed approach to solve five benchmark test problems are compared with those obtained by using the canonical genetic algorithm. Indeed, the proposed approach's performance is shown to surpass those of the canonical genetic algorithm. © 2001 OPA (Overseas Publishers Association) N.V. Published by license under the Gordon and Breach Science Publishers imprint, a member of the Taylor & Francis Group.en_HK
dc.languageengen_HK
dc.publisherTaylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/0305215x.aspen_HK
dc.relation.ispartofEngineering Optimizationen_HK
dc.subjectFunction optimizationen_HK
dc.subjectGenetic algorithmsen_HK
dc.subjectGenetic operatorsen_HK
dc.titleFunction optimization by using genetic algorithms with individuals having different birth and survival ratesen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0305-215X&volume=33&spage=777&epage=791&date=2001&atitle=Function+optimization+by+using+genetic+algorithms+with+individuals+having+different+birth+and+survival+ratesen_HK
dc.identifier.emailMak, KL:makkl@hkucc.hku.hken_HK
dc.identifier.authorityMak, KL=rp00154en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.scopuseid_2-s2.0-25844527230en_HK
dc.identifier.hkuros71941en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-25844527230&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume33en_HK
dc.identifier.issue6en_HK
dc.identifier.spage749en_HK
dc.identifier.epage777en_HK
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridMak, KL=7102680226en_HK
dc.identifier.scopusauthoridWong, YS=26637607500en_HK

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