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dc.contributor.authorTamjidul Hoque, Md.
dc.contributor.authorChetty, Madhu
dc.contributor.authorS. Dooley, Laurence
dc.date.accessioned2017-05-03T16:58:26Z
dc.date.available2017-05-03T16:58:26Z
dc.date.issued2007
dc.date.modified2010-02-09T05:29:35Z
dc.identifier.issn9783540752851
dc.identifier.doi10.1007/978-3-540-75286-8_9
dc.identifier.urihttp://hdl.handle.net/10072/28700
dc.description.abstractThe schemata theorem, on which the working of Genetic Algorithm (GA) is based in its current form, has a fallacious selection procedure and incomplete crossover operation. In this paper, generalization of the schemata theorem has been provided by correcting and removing these limitations. The analysis shows that similarity growth within GA population is inherent due to its stochastic nature. While the stochastic property helps in GA's convergence. The similarity growth is responsible for stalling and becomes more prevalent for hard optimization problem like protein structure prediction (PSP). While it is very essential that GA should explore the vast and complicated search landscape, in reality, it is often stuck in local minima. This paper shows that, removal of members of population having certain percentage of similarity would keep GA perform better, balancing and maintaining convergence property intact as well as avoids stalling.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.languageEnglish
dc.language.isoeng
dc.publisherSpringer
dc.publisher.placeGermany
dc.relation.ispartofchapter9
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofpagefrom84
dc.relation.ispartofpageto97
dc.relation.ispartofjournalLecture Notes in Computer Science
dc.relation.ispartofvolume4774
dc.rights.retentionY
dc.subject.fieldofresearchBioinformatics
dc.subject.fieldofresearchcode060102
dc.titleGeneralized Schemata Theorem Incorporating Twin Removal for Protein Structure Prediction
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.date.issued2007
gro.hasfulltextNo Full Text
gro.griffith.authorHoque, Md T.


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