ArticleAlgorithms for molecular biology : AMB2010
Gravitation field algorithm and its application in gene cluster.
Article in Algorithms for molecular biology : AMB, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges.Artificial intelligence review · 2023Article
- An Improved Electromagnetic Field Optimization for the Global Optimization Problems.Computational intelligence and neuroscience · 2019Article
- Article
- Inferring gene regulatory networks by singular value decomposition and gravitation field algorithm.PloS one · 2012Article
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundSearching 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.
resultsThis 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.
conclusionsThe 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 http://ccst.jlu.edu.cn/CSBG/GFA.
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