Limiting the Number Fitness Cases in Genetic Programming Using Statistics

Created by W.Langdon from gp-bibliography.bib Revision:1.2031

@InProceedings{giacobini:ppsn2002:pp371,
  author =       "Mario Giacobini and Marco Tomassini and 
                 Leonardo Vanneschi",
  title =        "Limiting the Number Fitness Cases in Genetic
                 Programming Using Statistics",
  booktitle =    "Parallel Problem Solving from Nature - PPSN VII",
  address =      "Granada, Spain",
  month =        "7-11 " # sep,
  pages =        "371--380",
  year =         "2002",
  editor =       "Juan J. Merelo-Guervos and Panagiotis Adamidis and 
                 Hans-Georg Beyer and Jose-Luis Fernandez-Villacanas and 
                 Hans-Paul Schwefel",
  number =       "2439",
  series =       "Lecture Notes in Computer Science, LNCS",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, Parameter
                 tuning, Fitness Evaluation, Theory of evolutionary
                 computing",
  ISBN =         "3-540-44139-5",
  annote =       "Available from
                 http://link.springer.de/link/service/series/0558/papers/2439/243900371.pdf",
  URL =          "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2439&spage=371",
  doi =          "doi:10.1007/3-540-45712-7_36",
  abstract =     "Fitness evaluation is often a time consuming activity
                 in genetic programming applications and it is thus of
                 interest to find criteria that can help in reducing the
                 time without compromising the quality of the results.
                 We use well-known results in statistics and information
                 theory to limit the number of fitness cases that are
                 needed for reliable function reconstruction in genetic
                 programming. By using two numerical examples, we show
                 that the results agree with our theoretical
                 predictions. Since our approach is problem-independent,
                 it can be used together with techniques for choosing an
                 efficient set of fitness cases.",
}

Genetic Programming entries for Mario Giacobini Marco Tomassini Leonardo Vanneschi