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

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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.

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Acknowledgement

This work is sponsored in part by the National Basic Research Program of China (973 Program) under Grant No. 2011CB302303, the National Natural Science Foundation of China (Grant Nos. 61272314, 60933002), National High Technology Research and Development Program of China (863 Program) under Grant No. 2013AA013203, the Specialized Research Fund for the Doctoral Program of Higher Education (Grant No. 20110145110010), the Excellent Youth Foundation of Hubei Scientific Committee (Grant No. 2012FFA025), the Program for New Century Excellent Talents in University (Grant No. NCET-11-0722), and Wuhan Chenguang Project (2013070104010019). The authors would also like to thank Dr. Siwei Jiang for the source code of SA-MGEP [20].

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Authors and Affiliations

  1. School of Computer Science, Huazhong University of Science & Technology, Wuhan, Hubei, 430074, China

    Yunliang Chen, Jianzhong Huang & Changsheng Xie

  2. School of Computer Science, China University of Geosciences, Wuhan, 430074, China

    Yunliang Chen & Dan Chen

  3. Department of Electrical and Computer Engineering, North Dakota State University, Fargo, ND, USA

    Samee U. Khan

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  1. Yunliang Chen
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  2. Dan Chen
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  3. Samee U. Khan
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  4. Jianzhong Huang
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  5. Changsheng Xie
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Correspondence to Dan Chen or Samee U. Khan.

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Chen, Y., Chen, D., Khan, S.U. et al. Solving symbolic regression problems with uniform design-aided gene expression programming. J Supercomput 66, 1553–1575 (2013). https://doi.org/10.1007/s11227-013-0943-6

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