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- Title
Solving symbolic regression problems with uniform design-aided gene expression programming.
- Authors
Chen, Yunliang; Chen, Dan; Khan, Samee U.; Huang, Jianzhong; Xie, Changsheng
- 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.
- Subjects
PROBLEM solving; EVOLUTIONARY algorithms; SYSTEMS design; COMPUTER programming; REGRESSION analysis; TIME Series Processor (Computer program language)
- Publication
Journal of Supercomputing, 2013, Vol 66, Issue 3, p1553
- ISSN
0920-8542
- Publication type
Article
- DOI
10.1007/s11227-013-0943-6