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GPTP 2009: An Example of Evolvability

  • Una-May O’Reilly
  • Trent McConaghy
  • Rick Riolo
Chapter
Part of the Genetic and Evolutionary Computation book series (GEVO)

Abstract

This introductory chapter gives a brief description of genetic programming (GP); summarizes 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 realized 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 modeling 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 aterial 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.

Keywords

Genetic Programming Evolutionary Computation Resource Usage Symbolic Regression Practice Versus 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Una-May O’Reilly
    • 1
  • Trent McConaghy
    • 2
  • Rick Riolo
    • 3
  1. 1.Computer Science and Artificial Intelligence LaboratoryMassachusetts Institute of TechnologyBostonUSA
  2. 2.Solido Design Automation Inc.SaskatoonCanada
  3. 3.Center for Study of Complex SystemsUniversity of MichiganMichiganUSA

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