Prefix Gene Expression Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3779

@InProceedings{Li:gecco05lbp,
  author =       "Xin Li and Chi Zhou and Weimin Xiao and 
                 Peter C. Nelson",
  title =        "Prefix Gene Expression Programming",
  booktitle =    "Late breaking paper at Genetic and Evolutionary
                 Computation Conference {(GECCO'2005)}",
  year =         "2005",
  month =        "25-29 " # jun,
  editor =       "Franz Rothlauf",
  address =      "Washington, D.C., USA",
  URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/85-li.pdf",
  URL =          "http://www.cs.uic.edu/~xli1/papers/PGEP_GECCOLateBreaking05_XLi.pdf",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming",
  abstract =     "Gene Expression Programming (GEP) is a powerful
                 evolutionary method derived from Genetic Programming
                 (GP) for model learning and knowledge discovery.
                 However, when dealing with complex problems, its
                 genotype under Karva notation does not allow
                 hierarchical composition of the solution, which impairs
                 the efficiency of the algorithm. We propose a new
                 representation scheme based on prefix notation that
                 overcomes the original GEP's drawbacks. The resulted
                 algorithm is called Prefix GEP (P-GEP). The major
                 advantages with P-GEP include the natural hierarchy in
                 forming the solutions and more protective genetic
                 operations for substructure components. An artificial
                 symbolic regression problem and a set of benchmark
                 classification problems from UCI machine learning
                 repository have been tested to demonstrate the
                 applicability of P-GEP. The results show that P-GEP
                 follows a faster fitness convergence curve and the
                 rules generated from P-GEP consistently achieve better
                 average classification accuracy compared with GEP",
  notes =        "Distributed on CD-ROM at GECCO-2005",
}

Genetic Programming entries for Xin Li Chi Zhou Weimin Xiao Peter C Nelson

Citations