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The Journal of Supercomputing

Erschienen in:

01.12.2013

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

verfasst von: Yunliang Chen, Dan Chen, Samee U. Khan, Jianzhong Huang, Changsheng Xie

Erschienen in: The Journal of Supercomputing | Ausgabe 3/2013

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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.

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Titel: Solving symbolic regression problems with uniform design-aided gene expression programming
verfasst von: Yunliang Chen
Dan Chen
Samee U. Khan
Jianzhong Huang
Changsheng Xie
Publikationsdatum: 01.12.2013
Verlag: Springer US
Erschienen in: The Journal of Supercomputing / Ausgabe 3/2013
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI: https://doi.org/10.1007/s11227-013-0943-6

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