Improving Means and Variances of Best-of-Run Programs in Genetic Programming

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

@InProceedings{moore:1998:imvbrpGP,
  author =       "Frank W. Moore",
  title =        "Improving Means and Variances of Best-of-Run Programs
                 in Genetic Programming",
  booktitle =    "Proceedings of the Ninth Midwest Artificial
                 Intelligence and Cognitive Science Conference
                 (MAICS-98)",
  year =         "1998",
  editor =       "M. W. Evens",
  pages =        "95--101",
  address =      "Russ Engineering Center, Wright State University,
                 Dayton, Ohio, USA",
  month =        "20-22 " # mar,
  publisher =    "AAAI Press",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.aaai.org/Library/MAICS/1998/maics98-013.php",
  URL =          "http://www.aaai.org/Papers/MAICS/1998/MAICS98-013.pdf",
  size =         "7 pages",
  abstract =     "Genetic programming (GP) systems have traditionally
                 used a fixed training population to evolve best-of-run
                 programs according to problem-specific fitness
                 criteria. The ideal GP training population would be
                 sufficiently representative of each of the potentially
                 difficult situations encountered during subsequent
                 program use to allow the resulting best-of-run programs
                 to handle each test situation in an optimized manner.
                 Practical considerations limit the size of the training
                 population, thus reducing the percentage of situations
                 explicitly anticipated by that population. As a result,
                 best-of-run programs may fail to exhibit sufficiently
                 optimized performance during subsequent program
                 testing. This paper summarizes an investigation into
                 the effects of creating a new randomly generated
                 training population prior to the fitness evaluation of
                 each generation of programs. Test results suggest that
                 this alternative approach to training can bolster
                 generalization of evolved solutions, improving the mean
                 program performance while significantly reducing
                 variance in the fitness of best-of-run programs.",
  notes =        "http://www.iue.indiana.edu/csci/maics98/",
}

Genetic Programming entries for Frank William Moore

Citations