Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.11861/7545
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Wong, Ka-Chun | en_US |
dc.contributor.author | Prof. LEUNG Kwong Sak | en_US |
dc.contributor.author | Wong, Man-Hon | en_US |
dc.date.accessioned | 2023-03-23T03:59:43Z | - |
dc.date.available | 2023-03-23T03:59:43Z | - |
dc.date.issued | 2010 | - |
dc.identifier.citation | Proceedings of the 12th Annual Genetic and Evolutionary Computation Conference, 2010, GECCO '10, pp. 15 - 22 | en_US |
dc.identifier.isbn | 978-145030072-8 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.11861/7545 | - |
dc.description.abstract | This 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.iso | en | en_US |
dc.publisher | Association for Computing Machinery (ACM) | en_US |
dc.relation.ispartof | Proceedings of the 12th Annual Genetic and Evolutionary Computation Conference, GECCO '10 | en_US |
dc.title | Protein structure prediction on a lattice model via multimodal optimization techniques | en_US |
dc.type | Conference Proceedings | en_US |
dc.identifier.doi | 10.1145/1830483.1830513 | - |
item.fulltext | No Fulltext | - |
crisitem.author.dept | Department of Applied Data Science | - |
Appears in Collections: | Applied Data Science - Publication |
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