Effective simplification of evolved push programs using a simple, stochastic hill-climber

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Lee Spector and Thomas Helmuth",
  title =        "Effective simplification of evolved push programs
                 using a simple, stochastic hill-climber",
  booktitle =    "GECCO Comp '14: Proceedings of the 2014 conference
                 companion on Genetic and evolutionary computation
  year =         "2014",
  editor =       "Christian Igel and Dirk V. Arnold and 
                 Christian Gagne and Elena Popovici and Anne Auger and 
                 Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and 
                 Kalyanmoy Deb and Benjamin Doerr and James Foster and 
                 Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and 
                 Hitoshi Iba and Christian Jacob and Thomas Jansen and 
                 Yaochu Jin and Marouane Kessentini and 
                 Joshua D. Knowles and William B. Langdon and Pedro Larranaga and 
                 Sean Luke and Gabriel Luque and John A. W. McCall and 
                 Marco A. {Montes de Oca} and Alison Motsinger-Reif and 
                 Yew Soon Ong and Michael Palmer and 
                 Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and 
                 Guenther Ruhe and Tom Schaul and Thomas Schmickl and 
                 Bernhard Sendhoff and Kenneth O. Stanley and 
                 Thomas Stuetzle and Dirk Thierens and Julian Togelius and 
                 Carsten Witt and Christine Zarges",
  isbn13 =       "978-1-4503-2881-4",
  keywords =     "genetic algorithms, genetic programming: Poster",
  pages =        "147--148",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Vancouver, BC, Canada",
  URL =          "http://doi.acm.org/10.1145/2598394.2598414",
  DOI =          "doi:10.1145/2598394.2598414",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Genetic programming systems often produce programs
                 that include unnecessary code. This is undesirable for
                 several reasons, including the burdens that
                 overly-large programs put on end-users for program
                 interpretation and maintenance. The problem is
                 exacerbated by recently developed techniques, such as
                 genetic programming with geometric semantic crossover,
                 that tend to produce enormous programs. Methods for
                 automatically simplifying evolved programs are
                 therefore of interest, but automatic simplification is
                 non-trivial in the context of traditional program
                 representations with unconstrained function sets. Here
                 we show how evolved programs expressed in the
                 stack-based Push programming language can be
                 automatically and reliably simplified using a simple,
                 stochastic hill-climber. We demonstrate and
                 quantitatively characterise this simplification process
                 on programs evolved to solve four non-trivial genetic
                 programming problems with qualitatively different
                 function sets.",
  notes =        "Also known as \cite{2598414} Distributed at

Genetic Programming entries for Lee Spector Thomas Helmuth