A stochastic method for controlling the scaling parameters of Cauchy mutation in fast evolutionary programming

Authors:
Yunji Chen;Ke Tang;Tianshi Chen
Affiliations:
Key Laboratory of Computer System and Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;Nature Inspired Computation and Applications Laboratory, Dept. of Computer Science and Technology, Univ. of Science and Technology of China, Hefei, Anhui, China;Nature Inspired Computation and Applications Laboratory, Dept. of Computer Science and Techn., Univ. of Science and Techn. of China, Hefei, Anhui, China
Venue:
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Year:
2009

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Abstract

The fast evolutionary programming (FEP) introduced the Cauchy distribution into its mutation operator, thus the performances of EP were promoted significantly on a number of benchmark problems. However, the scaling parameter of the Cauchy mutation is invariable, which has become an obstacle for FEP to reach better performance. This paper proposes and analyzes a new stochastic method for controlling the variable scaling parameters of Cauchy mutation. This stochastic method collects information from a group of individuals randomly selected from the population. Empirical evidence validates our method to be very helpful in promoting the performance of FEP.