Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/7545
DC FieldValueLanguage
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:Publication
Show simple item record

SCOPUSTM   
Citations

32
checked on Jan 3, 2024

Page view(s)

15
checked on Jan 3, 2024

Google ScholarTM

Impact Indices

Altmetric

PlumX

Metrics


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.