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dc.contributor.authorT. Hoque, Md.
dc.contributor.authorChetty, M.
dc.contributor.authorDooley, L.
dc.contributor.editorBettina Berendt
dc.date.accessioned2017-05-03T16:58:24Z
dc.date.available2017-05-03T16:58:24Z
dc.date.issued2007
dc.date.modified2009-12-05T05:18:22Z
dc.identifier.isbn9783642018909
dc.identifier.doi10.1007/978-3-540-73297-6_10
dc.identifier.urihttp://hdl.handle.net/10072/27111
dc.description.abstractProtein folding prediction (PFP), especially the ab initio approach, is one of the most challenging problems facing the bioinformatics research community due to it being extremely complex to solve and computationally very intensive. Hybrid evolutionary computing techniques have assumed considerable importance in attempting to overcome these challenges and so this chapter explores some of these PFP issues. By using the well-known Hydrophobic-Hydrophilic (HP) model, the performance of a number of contemporary nondeterministic search techniques are examined. Particular emphasis is given to the new Hybrid Genetic Algorithm (HGA) approach, which is shown to provide a number of performance benefits for PFP applications.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.languageEnglish
dc.language.isoeng
dc.publisherSpringer
dc.publisher.placeGermany
dc.relation.ispartofbooktitleHybrid Evolutionary Algorithms: Methodologies, Architectures, and Reviews
dc.relation.ispartofchapter10
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofpagefrom241
dc.relation.ispartofpageto268
dc.rights.retentionY
dc.subject.fieldofresearchBioinformatics
dc.subject.fieldofresearchcode060102
dc.titleSignificance of hybrid evolutionary computation for ab initio protein folding prediction
dc.typeBook chapter
dc.type.descriptionB1 - Chapters
dc.type.codeB - Book Chapters
gro.date.issued2007
gro.hasfulltextNo Full Text
gro.griffith.authorHoque, Md T.


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