A novel method for real parameter optimization based on Gene Expression Programming

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@Article{Xu2008,
  author =       "Kaikuo Xu and Yintian Liu and Rong Tang and 
                 Jie Zuo and Jun Zhu and Changjie Tang",
  title =        "A novel method for real parameter optimization based
                 on Gene Expression Programming",
  journal =      "Applied Soft Computing",
  year =         "2009",
  volume =       "9",
  number =       "2",
  pages =        "725--737",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming, Gene
                 Expression Programming, Real parameter optimization,
                 Expression tree",
  ISSN =         "1568-4946",
  DOI =          "doi:10.1016/j.asoc.2008.09.007",
  URL =          "http://www.sciencedirect.com/science/article/B6W86-4TMJ3TD-4/2/ddab66fae1f3b964599d5c56888dfcb5",
  abstract =     "Gene Expression Programming (GEP) is a new technique
                 of evolutionary algorithm that implements
                 genome/phoneme representation in computing programs.
                 Due to its power in global search, it is widely applied
                 in symbolic regression. However, little work has been
                 done to apply it to real parameter optimization yet.
                 This paper proposes a real parameter optimization
                 method named Uniform-Constants based GEP (UC-GEP). In
                 UC-GEP, the constant domain directly participates in
                 the evolution. Our research conducted extensive
                 experiments over nine benchmark functions from the IEEE
                 Congress on Evolutionary Computation 2005 and compared
                 the results to three other algorithms namely
                 Meta-Constants based GEP (MC-GEP),
                 Meta-Uniform-Constants based GEP (MUC-GEP), and the
                 Floating Point Genetic Algorithm (FP-GA). For
                 simplicity, all GEP methods adopt a one-tier index gene
                 structure. The results demonstrate the optimal
                 performance of our UC-GEP in solving multimodal
                 problems and show that at least one GEP method
                 outperforms FP-GA on all test functions with higher
                 computational complexity.",
}

Genetic Programming entries for Kaikuo Xu Jiangtao Qiu Rong Tang Jie Zuo Jun Zhu Changjie Tang

Citations