Study on the Optimize Strategies of Gene Expression Programming

Abstract:

The purpose of this paper is to improve the efficiency of the Gene Expression Programming (GEP) algorithm. The GEP algorithm is an evolutionary computation. It inherits the characteristics of Genetic Algorithm and Genetic Programming. Through its own characteristics, the GEP algorithm can get the optimal solution of the complicated problem. So, the GEP algorithm has achieved good results in many areas. However, there are also some inevitable drawbacks about the GEP algorithm itself. This paper proposes 5 deficiencies aspects of the GEP algorithm (expression meaning, fitness calculation, local convergence, variable selection, genetic operations, selection of genetic operation rates), and gives the corresponding solutions.

• Periodical: Applied Mechanics and Materials (Volume 432)
• Pages: 565-570
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.432.565
• Online since: September 2013
• Authors: Xin Wen Gao, Ben Bo Guan, Xing Jian Guan
• Keywords: Evolutionary Computation, Fitness Calculation, Genetic Operations, GEP, Local Convergence, Variables Selection

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