Gravitation field algorithm and its application in gene cluster.
BACKGROUND
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.


RESULTS
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.


CONCLUSIONS
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 http://ccst.jlu.edu.cn/CSBG/GFA.
Zheng
Ming
M
College of Computer Science and Technology, Jilin University, Changchun, 130012, China.
Liu
Gui-Xia
GX
College of Computer Science and Technology, Jilin University, Changchun, 130012, China.
Zhou
Chun-Guang
CG
College of Computer Science and Technology, Jilin University, Changchun, 130012, China.
Liang
Yan-Chun
YC
College of Computer Science and Technology, Jilin University, Changchun, 130012, China.
Wang
Yan
Y
College of Computer Science and Technology, Jilin University, Changchun, 130012, China.