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

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{journals/tjs/ChenCKHX13,
  title =        "Solving symbolic regression problems with uniform
                 design-aided gene expression programming",
  author =       "Yunliang Chen and Dan Chen and Samee Ullah Khan and 
                 Jianzhong Huang and Changsheng Xie",
  journal =      "The Journal of Supercomputing",
  year =         "2013",
  number =       "3",
  volume =       "66",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming, GEP",
  bibdate =      "2013-11-11",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/tjs/tjs66.html#ChenCKHX13",
  pages =        "1553--1575",
  URL =          "http://dx.doi.org/10.1007/s11227-013-0943-6",
  size =         "23 pages",
  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.",
}

Genetic Programming entries for Yunliang Chen Dan Chen Samee Ullah Khan Jianzhong Huang Changsheng Xie

Citations