Solving symbolic regression problems with uniform design-aided gene expression programming

Authors:
Yunliang Chen;Dan Chen;Samee U. Khan;Jianzhong Huang;Changsheng Xie
Affiliations:
School of Computer Science, Huazhong University of Science & Technology, Wuhan, China 430074 and School of Computer Science, China University of Geosciences, Wuhan, China 430074;School of Computer Science, China University of Geosciences, Wuhan, China 430074;Department of Electrical and Computer Engineering, North Dakota State University, Fargo, USA;School of Computer Science, Huazhong University of Science & Technology, Wuhan, China 430074;School of Computer Science, Huazhong University of Science & Technology, Wuhan, China 430074
Venue:
The Journal of Supercomputing
Year:
2013

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Abstract

Gene Expression Programming (GEP) significantly surpasses traditional evolutionary approaches to solving symbolic regression problems. However, existing GEP algorithms still suffer from premature convergence and slow evolution in anaphase. Aiming at these pitfalls, we designed a novel evolutionary algorithm, namely Uniform Design-Aided Gene Expression Programming (UGEP). UGEP uses (1) a mixed-level uniform table for generating initial population and (2) multiparent crossover operators by taking advantages of the dispersibility of uniform design. In addition to a theoretic analysis, we compared UGEP to existing GEP variants via a number of experiments in dealing with symbolic regression problems including function fitting and chaotic time series prediction. Experimental results indicate that UGEP excels in terms of both the capability of achieving the global optimum and the convergence speed in solving symbolic regression problems.