Self-Learning Gene Expression Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Jinghui Zhong and Yew-Soon Ong and Wentong Cai",
  title =        "Self-Learning Gene Expression Programming",
  journal =      "IEEE Transactions on Evolutionary Computation",
  year =         "2016",
  volume =       "20",
  number =       "1",
  pages =        "65--78",
  month =        feb,
  keywords =     "genetic algorithms, genetic programming, Gene
                 Expression Programming, Even Parity Problem,
                 Evolutionary Computation, Symbolic Regression Problem,
                 GEP-ADF, TreeDE",
  ISSN =         "1089-778X",
  DOI =          "doi:10.1109/TEVC.2015.2424410",
  size =         "16 pages",
  abstract =     "In this paper, a novel self-learning gene expression
                 programming (GEP) methodology named SL-GEP is proposed
                 to improve the search accuracy and efficiency of GEP.
                 In contrast to the existing GEP variants, the proposed
                 SL-GEP features a novel chromosome representation where
                 each chromosome is embedded with subfunctions that can
                 be deployed to construct the final solution. As part of
                 the chromosome, the subfunctions are self-learned or
                 self-evolved by the proposed algorithm during the
                 evolutionary search. By encompassing subfunctions or
                 any partial solution as input arguments of another
                 subfunction, the proposed SL-GEP facilitates the
                 formation of sophisticated, higher-order, and
                 constructive subfunctions that improve the accuracy and
                 efficiency of the search. Further, a novel search
                 mechanism based on differential evolution is proposed
                 for the evolution of chromosomes in the SL-GEP. The
                 proposed SL-GEP is simple, generic and has much fewer
                 control parameters than the traditional GEP variants.
                 The proposed SL-GEP is validated on 15 symbolic
                 regression problems and six even parity problems.
                 Experimental results show that the proposed SL-GEP
                 offers enhanced performances over several
                 state-of-the-art algorithms in terms of accuracy and
                 search efficiency.",
  notes =        "School of Computer Engineering, Nanyang Technological
                 University, Singapore.

                 Also known as \cite{7089238}",

Genetic Programming entries for Jinghui Zhong Yew-Soon Ong Wentong Cai