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Article: A Markov chain analysis of genetic algorithms with individuals having different birth and survival rates

TitleA Markov chain analysis of genetic algorithms with individuals having different birth and survival rates
Authors
KeywordsFunction optimization
Genetic algorithms
Genetic operators
Markov chain analysis
Issue Date2005
PublisherTaylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/0305215x.asp
Citation
Engineering Optimization, 2005, v. 37 n. 6, p. 571-589 How to Cite?
AbstractThis article studies the convergence characteristics of a genetic algorithm (GA) in which individuals of different age groups in the population possess different survival and birth rates. The inclusion of this feature into the algorithm makes the algorithm mimic the natural evolutionary process more closely than the conventional GA. Although numerical experiments have demonstrated that the proposed algorithm tends to perform better than the conventional GA when used as a function optimizer, the population size of the algorithm is affected by the survival and birth rates of the individuals, which may lead to an unstable search process. Hence, this research develops the condition which governs the birth and survival rates for maintaining a stationary population size during the search process. The Markov chain approach is also used to analyze the convergence characteristics of the algorithm. The proposed algorithm is shown to converge to the global optimal solution if the best candidate solution is maintained over time. The mathematical analysis thus provides a theoretical foundation for the application of the proposed approach as a function optimizer. The performance of the proposed algorithm is tested by solving two benchmark test problems and the results are compared to those obtained by using the conventional GA. Indeed, comparison of the results clearly shows that the proposed approach is superior to the canonical genetic algorithm in terms of the quality of the final solution. The algorithm is described in some detail in the hope of thus stimulating the use of the proposed genetic approach to the solution of important problems in industrial engineering practice. © 2005 Taylor & Francis Group Ltd.
Persistent Identifierhttp://hdl.handle.net/10722/74330
ISSN
2015 Impact Factor: 1.38
2015 SCImago Journal Rankings: 0.866
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorMak, KLen_HK
dc.contributor.authorLau, JSKen_HK
dc.contributor.authorWei, Cen_HK
dc.date.accessioned2010-09-06T07:00:16Z-
dc.date.available2010-09-06T07:00:16Z-
dc.date.issued2005en_HK
dc.identifier.citationEngineering Optimization, 2005, v. 37 n. 6, p. 571-589en_HK
dc.identifier.issn0305-215Xen_HK
dc.identifier.urihttp://hdl.handle.net/10722/74330-
dc.description.abstractThis article studies the convergence characteristics of a genetic algorithm (GA) in which individuals of different age groups in the population possess different survival and birth rates. The inclusion of this feature into the algorithm makes the algorithm mimic the natural evolutionary process more closely than the conventional GA. Although numerical experiments have demonstrated that the proposed algorithm tends to perform better than the conventional GA when used as a function optimizer, the population size of the algorithm is affected by the survival and birth rates of the individuals, which may lead to an unstable search process. Hence, this research develops the condition which governs the birth and survival rates for maintaining a stationary population size during the search process. The Markov chain approach is also used to analyze the convergence characteristics of the algorithm. The proposed algorithm is shown to converge to the global optimal solution if the best candidate solution is maintained over time. The mathematical analysis thus provides a theoretical foundation for the application of the proposed approach as a function optimizer. The performance of the proposed algorithm is tested by solving two benchmark test problems and the results are compared to those obtained by using the conventional GA. Indeed, comparison of the results clearly shows that the proposed approach is superior to the canonical genetic algorithm in terms of the quality of the final solution. The algorithm is described in some detail in the hope of thus stimulating the use of the proposed genetic approach to the solution of important problems in industrial engineering practice. © 2005 Taylor & Francis Group Ltd.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.subjectMarkov chain analysisen_HK
dc.titleA Markov chain analysis of 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=37&issue=6&spage=571&epage=589&date=2005&atitle=A+Markov+chain+analysis+of+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.doi10.1080/03052150500114263en_HK
dc.identifier.scopuseid_2-s2.0-24944579269en_HK
dc.identifier.hkuros107605en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-24944579269&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume37en_HK
dc.identifier.issue6en_HK
dc.identifier.spage571en_HK
dc.identifier.epage589en_HK
dc.identifier.isiWOS:000231982900002-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridMak, KL=7102680226en_HK
dc.identifier.scopusauthoridLau, JSK=8982533400en_HK
dc.identifier.scopusauthoridWei, C=7401658059en_HK
dc.identifier.citeulike308690-

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