Gravitation field algorithm and its application in gene cluster
<p>Abstract</p> <p>Background</p> <p>Searching optima is one of the most challenging tasks in clustering genes from available experimental data or given functions. SA, GA, PSO and other similar efficient global optimization methods are used by biotechnologists. All thes...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2010-09-01
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| Series: | Algorithms for Molecular Biology |
| Online Access: | http://www.almob.org/content/5/1/32 |
| _version_ | 1818960298727440384 |
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| author | Zheng Ming Liu Gui-xia Zhou Chun-guang Liang Yan-chun Wang Yan |
| author_facet | Zheng Ming Liu Gui-xia Zhou Chun-guang Liang Yan-chun Wang Yan |
| author_sort | Zheng Ming |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>Searching optima is one of the most challenging tasks in clustering genes from available experimental data or given functions. SA, GA, PSO and other similar efficient global optimization methods are used by biotechnologists. All these algorithms are based on the imitation of natural phenomena.</p> <p>Results</p> <p>This paper proposes a novel searching optimization algorithm called Gravitation Field Algorithm (GFA) which is derived from the famous astronomy theory Solar Nebular Disk Model (SNDM) of planetary formation. GFA simulates the Gravitation field and outperforms GA and SA in some multimodal functions optimization problem. And GFA also can be used in the forms of unimodal functions. GFA clusters the dataset well from the Gene Expression Omnibus.</p> <p>Conclusions</p> <p>The mathematical proof demonstrates that GFA could be convergent in the global optimum by probability 1 in three conditions for one independent variable mass functions. In addition to these results, the fundamental optimization concept in this paper is used to analyze how SA and GA affect the global search and the inherent defects in SA and GA. Some results and source code (in Matlab) are publicly available at <url>http://ccst.jlu.edu.cn/CSBG/GFA</url>.</p> |
| first_indexed | 2024-12-20T11:55:19Z |
| format | Article |
| id | doaj.art-3ea51fc68bf0436c9f51903c982e1737 |
| institution | Directory Open Access Journal |
| issn | 1748-7188 |
| language | English |
| last_indexed | 2024-12-20T11:55:19Z |
| publishDate | 2010-09-01 |
| publisher | BMC |
| record_format | Article |
| series | Algorithms for Molecular Biology |
| spelling | doaj.art-3ea51fc68bf0436c9f51903c982e17372022-12-21T19:41:41ZengBMCAlgorithms for Molecular Biology1748-71882010-09-01513210.1186/1748-7188-5-32Gravitation field algorithm and its application in gene clusterZheng MingLiu Gui-xiaZhou Chun-guangLiang Yan-chunWang Yan<p>Abstract</p> <p>Background</p> <p>Searching optima is one of the most challenging tasks in clustering genes from available experimental data or given functions. SA, GA, PSO and other similar efficient global optimization methods are used by biotechnologists. All these algorithms are based on the imitation of natural phenomena.</p> <p>Results</p> <p>This paper proposes a novel searching optimization algorithm called Gravitation Field Algorithm (GFA) which is derived from the famous astronomy theory Solar Nebular Disk Model (SNDM) of planetary formation. GFA simulates the Gravitation field and outperforms GA and SA in some multimodal functions optimization problem. And GFA also can be used in the forms of unimodal functions. GFA clusters the dataset well from the Gene Expression Omnibus.</p> <p>Conclusions</p> <p>The mathematical proof demonstrates that GFA could be convergent in the global optimum by probability 1 in three conditions for one independent variable mass functions. In addition to these results, the fundamental optimization concept in this paper is used to analyze how SA and GA affect the global search and the inherent defects in SA and GA. Some results and source code (in Matlab) are publicly available at <url>http://ccst.jlu.edu.cn/CSBG/GFA</url>.</p>http://www.almob.org/content/5/1/32 |
| spellingShingle | Zheng Ming Liu Gui-xia Zhou Chun-guang Liang Yan-chun Wang Yan Gravitation field algorithm and its application in gene cluster Algorithms for Molecular Biology |
| title | Gravitation field algorithm and its application in gene cluster |
| title_full | Gravitation field algorithm and its application in gene cluster |
| title_fullStr | Gravitation field algorithm and its application in gene cluster |
| title_full_unstemmed | Gravitation field algorithm and its application in gene cluster |
| title_short | Gravitation field algorithm and its application in gene cluster |
| title_sort | gravitation field algorithm and its application in gene cluster |
| url | http://www.almob.org/content/5/1/32 |
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