Semantically embedded genetic programming: automated design of abstract program representations

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

@InProceedings{Krawiec:2011:GECCO,
  author =       "Krzysztof Krawiec",
  title =        "Semantically embedded genetic programming: automated
                 design of abstract program representations",
  booktitle =    "GECCO '11: Proceedings of the 13th annual conference
                 on Genetic and evolutionary computation",
  year =         "2011",
  editor =       "Natalio Krasnogor and Pier Luca Lanzi and 
                 Andries Engelbrecht and David Pelta and Carlos Gershenson and 
                 Giovanni Squillero and Alex Freitas and 
                 Marylyn Ritchie and Mike Preuss and Christian Gagne and 
                 Yew Soon Ong and Guenther Raidl and Marcus Gallager and 
                 Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and 
                 Nikolaus Hansen and Silja Meyer-Nieberg and 
                 Jim Smith and Gus Eiben and Ester Bernado-Mansilla and 
                 Will Browne and Lee Spector and Tina Yu and Jeff Clune and 
                 Greg Hornby and Man-Leung Wong and Pierre Collet and 
                 Steve Gustafson and Jean-Paul Watson and 
                 Moshe Sipper and Simon Poulding and Gabriela Ochoa and 
                 Marc Schoenauer and Carsten Witt and Anne Auger",
  isbn13 =       "978-1-4503-0557-0",
  pages =        "1379--1386",
  keywords =     "genetic algorithms, genetic programming",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Dublin, Ireland",
  DOI =          "doi:10.1145/2001576.2001762",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "We propose an alternative program representation that
                 relies on automatic semantic-based embedding of
                 programs into discrete multidimensional spaces. An
                 embedding imposes a well-structured hypercube topology
                 on the search space, endows it with a semantic-aware
                 neighborhood, and enables convenient search using
                 Cartesian coordinates. The embedding algorithm consists
                 in locality-driven optimization and operates in
                 abstraction from a specific fitness function, improving
                 locality of all possible fitness landscapes
                 simultaneously. We experimentally validate the approach
                 on a large sample of symbolic regression tasks and show
                 that it provides better search performance than the
                 original program space. We demonstrate also that
                 semantic embedding of small programs can be exploited
                 in a compositional manner to effectively search the
                 space of compound programs.",
  notes =        "Surjective

                 Also known as \cite{2001762} GECCO-2011 A joint meeting
                 of the twentieth international conference on genetic
                 algorithms (ICGA-2011) and the sixteenth annual genetic
                 programming conference (GP-2011)",
}

Genetic Programming entries for Krzysztof Krawiec

Citations