Investigation of Constant Creation Techniques in the Context of Gene Expression Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{li:2004:lbp,
  author =       "Xin Li and Chi Zhou and Peter C. Nelson and 
                 Thomas M. Tirpak",
  title =        "Investigation of Constant Creation Techniques in the
                 Context of Gene Expression Programming",
  booktitle =    "Late Breaking Papers at the 2004 Genetic and
                 Evolutionary Computation Conference",
  year =         "2004",
  editor =       "Maarten Keijzer",
  address =      "Seattle, Washington, USA",
  month =        "26 " # jul,
  keywords =     "genetic algorithms, genetic programming, GEP",
  URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2004/LBP023.pdf",
  URL =          "http://www.cs.uic.edu/~xli1/papers/GEPConstantCreation(GECCO04_LBP).pdf",
  abstract =     "Gene Expression Programming (GEP) is a new technique
                 of Genetic Programming (GP) that implements a linear
                 genotype representation. It uses fixed-length
                 chromosomes to represent expression trees of different
                 shapes and sizes, which results in unconstrained search
                 of the genome space while still ensuring validity of
                 the programs output. However, GEP has some difficulty
                 in discovering suitable function structures because the
                 genetic operators are more disruptive than traditional
                 tree-based GP. One possible remedy is to specifically
                 assist the algorithm in discovering useful numeric
                 constants. In this paper, the effectiveness of several
                 constant creation techniques for GEP has been
                 investigated through two symbolic regression benchmark
                 problems. Our experimental results show that constant
                 creation methods applied to the whole population for
                 selected generations perform better than methods that
                 are applied only to the best individuals. The proposed
                 tune-up process for the entire population can
                 significantly improve the average fitness of the best
                 solutions.",
  notes =        "Part of \cite{keijzer:2004:GECCO:lbp}",
}

Genetic Programming entries for Xin Li Chi Zhou Peter C Nelson Thomas M Tirpak

Citations