GPTP 2009: An Example of Evolvability

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

@InCollection{O'Reilly:2009:GPTP,
  author =       "Una-May O'Reilly and Trent McConaghy and Rick Riolo",
  title =        "GPTP 2009: An Example of Evolvability",
  booktitle =    "Genetic Programming Theory and Practice {VII}",
  year =         "2009",
  editor =       "Rick L. Riolo and Una-May O'Reilly and 
                 Trent McConaghy",
  series =       "Genetic and Evolutionary Computation",
  address =      "Ann Arbor",
  month =        "14-16 " # may,
  publisher =    "Springer",
  chapter =      "1",
  pages =        "1--18",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-4419-1653-2",
  DOI =          "doi:10.1007/978-1-4419-1626-6_1",
  URL =          "http://trent.st/content/2009-GPTP-aims.pdf",
  size =         "18 pages",
  abstract =     "This introductory chapter gives a brief description of
                 genetic programming (GP); summarises current GP
                 algorithm aims, issues, and progress; and finally
                 reviews the contributions of this volume, which were
                 presented at the GP Theory and Practice (GPTP) 2009
                 workshop.

                 This year marks a transition wherein the aims of GP
                 algorithms-reasonable resource usage, high quality
                 results, and reliable convergence-are being
                 consistently realised on an impressive variety of
                 real-world applications by skilled practitioners in the
                 field. These aims have been realized due to GP
                 researchers' growing collective understanding of the
                 nature of GP problems which require search across
                 spaces which are massive, multi-modal, and with poor
                 locality, and how that relates to long-discussed GP
                 issues such as bloat and premature convergence. New
                 ways to use and extend GP for improved computational
                 resource usage, quality of results, and reliability are
                 appearing and gaining momentum. These include: reduced
                 resource usage via rationally designed search spaces
                 and fitness functions for specific applications such as
                 induction of implicit functions or modelling stochastic
                 processes arising from bio-networks; improved quality
                 of results by explicitly targeting the interpretability
                 or trustworthiness of the final results; and heightened
                 reliability via consistently introducing new genetic
                 material in a structured manner or via coevolution and
                 teaming. These new developments highlight that GP's
                 challenges have changed from simply making it work on
                 smaller problems, to consistently and rapidly getting
                 high-quality results on large real-world problems. GPTP
                 2009 was a forum to advance GP's state of the art and
                 its contributions demonstrate how these aims can be met
                 on a variety of difficult problems.",
  notes =        "part of \cite{Riolo:2009:GPTP}",
}

Genetic Programming entries for Una-May O'Reilly Trent McConaghy Rick L Riolo

Citations