Behavioral programming: a broader and more detailed take on semantic GP

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Krzysztof Krawiec and Una-May O'Reilly",
  title =        "Behavioral programming: a broader and more detailed
                 take on semantic GP",
  booktitle =    "GECCO '14: Proceedings of the 2014 conference 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 =       "978-1-4503-2662-9",
  pages =        "935--942",
  keywords =     "genetic algorithms, genetic programming",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Vancouver, BC, Canada",
  note =         "Best paper",
  URL =          "",
  DOI =          "doi:10.1145/2576768.2598288",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "In evolutionary computation, the fitness of a
                 candidate solution conveys sparse feedback. Yet in many
                 cases, candidate solutions can potentially yield more
                 information. In genetic programming (GP), one can
                 easily examine program behaviour on particular fitness
                 cases or at intermediate execution states. However, how
                 to exploit it to effectively guide the search remains
                 unclear. In this study we apply machine learning
                 algorithms to features describing the intermediate
                 behavior of the executed program. We then drive the
                 standard evolutionary search with additional objectives
                 reflecting this intermediate behavior. The machine
                 learning functions independent of task-specific
                 knowledge and discovers potentially useful components
                 of solutions (subprograms), which we preserve in an
                 archive and use as building blocks when composing new
                 candidate solutions. In an experimental assessment on a
                 suite of benchmarks, the proposed approach proves more
                 capable of finding optimal and/or well-performing
                 solutions than control methods.",
  notes =        "Also known as \cite{2598288} GECCO-2014 A joint
                 meeting of the twenty third international conference on
                 genetic algorithms (ICGA-2014) and the nineteenth
                 annual genetic programming conference (GP-2014)",

Genetic Programming entries for Krzysztof Krawiec Una-May O'Reilly