The study of genetic information flux network properties in genetic algorithms

Zhengping Wu (), Qiong Xu, Gaosheng Ni and Gaoming Yu
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Zhengping Wu: College of Electrical Engineering and New Energy, China Three Gorges University, Hubei, Yichang, 443002, P. R. China
Qiong Xu: College of Electrical Engineering and New Energy, China Three Gorges University, Hubei, Yichang, 443002, P. R. China
Gaosheng Ni: College of Foreign Languages, China Three Gorges University, Hubei, Yichang 443002, P. R. China
Gaoming Yu: College of Petroleum Engineering, Yangtze University, Hubei, Wuhan 430100, P. R. China

International Journal of Modern Physics C (IJMPC), 2015, vol. 26, issue 07, 1-12

Abstract: In this paper, an empirical analysis is done on the information flux network (IFN) statistical properties of genetic algorithms (GA) and the results suggest that the node degree distribution of IFN is scale-free when there is at least some selection pressure, and it has two branches as node degree is small. Increasing crossover, decreasing the mutation rate or decreasing the selective pressure will increase the average node degree, thus leading to the decrease of scaling exponent. These studies will be helpful in understanding the combination and distribution of excellent gene segments of the population in GA evolving, and will be useful in devising an efficient GA.

Keywords: Genetic algorithms; information flux network; scale-free network; maximum likelihood estimation; scaling exponent; 11.25.Hf; 123.1K (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1142/S012918311550076X

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