Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/7545
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dc.contributor.authorWong, Ka-Chunen_US
dc.contributor.authorProf. LEUNG Kwong Saken_US
dc.contributor.authorWong, Man-Honen_US
dc.date.accessioned2023-03-23T03:59:43Z-
dc.date.available2023-03-23T03:59:43Z-
dc.date.issued2010-
dc.identifier.citationProceedings of the 12th Annual Genetic and Evolutionary Computation Conference, 2010, GECCO '10, pp. 15 - 22en_US
dc.identifier.isbn978-145030072-8-
dc.identifier.urihttp://hdl.handle.net/20.500.11861/7545-
dc.description.abstractThis paper considers the protein structure prediction problem as a multimodal optimization problem. In particular, de novo protein structure prediction problems on the 3D Hydrophobic-Polar (HP) lattice model are tackled by evolutionary algorithms using multimodal optimization techniques. In addition, a new mutation approach and performance metric are proposed for the problem. The experimental results indicate that the proposed algorithms are more effective than the state-of-the-arts algorithms, even though they are simple. Copyright 2010 ACM.en_US
dc.language.isoenen_US
dc.publisherAssociation for Computing Machinery (ACM)en_US
dc.relation.ispartofProceedings of the 12th Annual Genetic and Evolutionary Computation Conference, GECCO '10en_US
dc.titleProtein structure prediction on a lattice model via multimodal optimization techniquesen_US
dc.typeConference Proceedingsen_US
dc.identifier.doi10.1145/1830483.1830513-
item.fulltextNo Fulltext-
crisitem.author.deptDepartment of Applied Data Science-
Appears in Collections:Applied Data Science - Publication
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