Methods for Evolving Robust Programs

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

@InProceedings{panait:2003:gecco,
  author =       "Liviu Panait and Sean Luke",
  title =        "Methods for Evolving Robust Programs",
  booktitle =    "Genetic and Evolutionary Computation -- GECCO-2003",
  editor =       "E. Cant{\'u}-Paz and J. A. Foster and K. Deb and 
                 D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and 
                 R. Standish and G. Kendall and S. Wilson and 
                 M. Harman and J. Wegener and D. Dasgupta and M. A. Potter and 
                 A. C. Schultz and K. Dowsland and N. Jonoska and 
                 J. Miller",
  year =         "2003",
  pages =        "1740--1751",
  address =      "Chicago",
  publisher_address = "Berlin",
  month =        "12-16 " # jul,
  volume =       "2724",
  series =       "LNCS",
  ISBN =         "3-540-40603-4",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://cs.gmu.edu/~lpanait/papers/panait03methods.pdf",
  DOI =          "doi:10.1007/3-540-45110-2_66",
  abstract =     "Many evolutionary computation search spaces require
                 fitness assessment through the sampling of and
                 generalization over a large set of possible cases as
                 input. Such spaces seem particularly apropos to Genetic
                 Programming, which notionally searches for computer
                 algorithms and functions. Most existing research in
                 this area uses ad-hoc approaches to the sampling task,
                 guided more by intuition than understanding. In this
                 initial investigation, we compare six approaches to
                 sampling large training case sets in the context of
                 genetic programming representations. These approaches
                 include fixed and random samples, and adaptive methods
                 such as coevolution or fitness sharing. Our results
                 suggest that certain domain features may lead to the
                 preference of one approach to generalization over
                 others. In particular, coevolution methods are strongly
                 domain-dependent. We conclude the paper with
                 suggestions for further investigations to shed more
                 light onto how one might adjust fitness assessment to
                 make various methods more effective.",
  notes =        "GECCO-2003. A joint meeting of the twelfth
                 International Conference on Genetic Algorithms
                 (ICGA-2003) and the eighth Annual Genetic Programming
                 Conference (GP-2003)",
}

Genetic Programming entries for Liviu Panait Sean Luke

Citations