Operator Choice and the Evolution of Robust Solutions

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

  author =       "Terence Soule",
  title =        "Operator Choice and the Evolution of Robust
  booktitle =    "Genetic Programming Theory and Practice",
  publisher =    "Kluwer",
  year =         "2003",
  editor =       "Rick L. Riolo and Bill Worzel",
  chapter =      "16",
  pages =        "257--269",
  keywords =     "genetic algorithms, genetic programming, Code growth,
                 code bloat, operators, introns, design, robust
  ISBN =         "1-4020-7581-2",
  URL =          "http://www2.cs.uidaho.edu/~tsoule/research/chap16.pdf",
  URL =          "http://www.springer.com/computer/ai/book/978-1-4020-7581-0",
  DOI =          "doi:10.1007/978-1-4419-8983-3_16",
  abstract =     "This research demonstrates that evolutionary pressure
                 favouring robust solutions has a significant impact on
                 the evolutionary process. More robust solutions are
                 solutions that are less likely to be degraded by the
                 genetic operators. This pressure for robust solutions
                 can be used to explain a number of evolutionary
                 behaviours. The experiments examine the effect of
                 different types and rates of genetic operators on the
                 evolution of robust solutions. Previously robustness
                 was observed to occur through an increase in
                 inoperative genes (introns). This work shows that
                 alternative strategies to increase robustness can
                 evolve. The results also show that different genetic
                 operators lead to different strategies for improving
                 robustness. These results can be useful in designing
                 genetic operators to encourage particular evolutionary
  notes =        "Part of \cite{RioloWorzel:2003}",

Genetic Programming entries for Terence Soule