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Multiple sequence alignment using particle swarm optimization
Dissertation (MSc)--University of Pretoria, 2009.
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| Format: | Thesis |
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University of Pretoria
2013
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| _version_ | 1867613717075591169 |
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| access_status_str | Open Access |
| author2 | Engelbrecht, Andries P. |
| author_browse | Engelbrecht, Andries P. |
| author_facet | Engelbrecht, Andries P. |
| collection | Thesis |
| dc_rights_str_mv | ©University of Pretoria 2007 E1190/ |
| description | Dissertation (MSc)--University of Pretoria, 2009. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/23406 |
| institution | University of Pretoria (South Africa) |
| last_indexed | 2026-06-10T12:40:34.602Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2013 |
| publishDateRange | 2013 |
| publishDateSort | 2013 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/23406 Multiple sequence alignment using particle swarm optimization Engelbrecht, Andries P. fabulon@gmail.com Zablocki, Fabien Bernard Roman Computational intelligence Particle swarm optimization (PSO) Bioinformatics Artificial intelligence Multi sequence alignment Deoxyribonucleic acid (DNA) UCTD Dissertation (MSc)--University of Pretoria, 2009. The recent advent of bioinformatics has given rise to the central and recurrent problem of optimally aligning biological sequences. Many techniques have been proposed in an attempt to solve this complex problem with varying degrees of success. This thesis investigates the application of a computational intelligence technique known as particle swarm optimization (PSO) to the multiple sequence alignment (MSA) problem. Firstly, the performance of the standard PSO (S-PSO) and its characteristics are fully analyzed. Secondly, a scalability study is conducted that aims at expanding the S-PSO’s application to complex MSAs, as well as studying the behaviour of three other kinds of PSOs on the same problems. Experimental results show that the PSO is efficient in solving the MSA problem and compares positively with well-known CLUSTAL X and T-COFFEE. Computer Science Unrestricted 2013-09-06T15:17:15Z 2009-04-08 2013-09-06T15:17:15Z 2008-09-02 2009-04-08 2009-01-16 Dissertation 2007 E1190/gm http://hdl.handle.net/2263/23406 http://upetd.up.ac.za/thesis/available/etd-01162009-131115/ ©University of Pretoria 2007 E1190/ application/pdf University of Pretoria |
| spellingShingle | Computational intelligence Particle swarm optimization (PSO) Bioinformatics Artificial intelligence Multi sequence alignment Deoxyribonucleic acid (DNA) UCTD Multiple sequence alignment using particle swarm optimization |
| title | Multiple sequence alignment using particle swarm optimization |
| title_full | Multiple sequence alignment using particle swarm optimization |
| title_fullStr | Multiple sequence alignment using particle swarm optimization |
| title_full_unstemmed | Multiple sequence alignment using particle swarm optimization |
| title_short | Multiple sequence alignment using particle swarm optimization |
| title_sort | multiple sequence alignment using particle swarm optimization |
| topic | Computational intelligence Particle swarm optimization (PSO) Bioinformatics Artificial intelligence Multi sequence alignment Deoxyribonucleic acid (DNA) UCTD |
| url | http://hdl.handle.net/2263/23406 http://upetd.up.ac.za/thesis/available/etd-01162009-131115/ |