Approximating geometric crossover by semantic backpropagation

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

@InProceedings{Krawiec:2013:GECCO,
  author =       "Krzysztof Krawiec and Tomasz Pawlak",
  title =        "Approximating geometric crossover by semantic
                 backpropagation",
  booktitle =    "GECCO '13: Proceeding of the fifteenth annual
                 conference on Genetic and evolutionary computation
                 conference",
  year =         "2013",
  editor =       "Christian Blum and Enrique Alba and Anne Auger and 
                 Jaume Bacardit and Josh Bongard and Juergen Branke and 
                 Nicolas Bredeche and Dimo Brockhoff and 
                 Francisco Chicano and Alan Dorin and Rene Doursat and 
                 Aniko Ekart and Tobias Friedrich and Mario Giacobini and 
                 Mark Harman and Hitoshi Iba and Christian Igel and 
                 Thomas Jansen and Tim Kovacs and Taras Kowaliw and 
                 Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and 
                 John McCall and Alberto Moraglio and 
                 Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and 
                 Gustavo Olague and Yew-Soon Ong and 
                 Michael E. Palmer and Gisele Lobo Pappa and 
                 Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and 
                 Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and 
                 Daniel Tauritz and Leonardo Vanneschi",
  isbn13 =       "978-1-4503-1963-8",
  pages =        "941--948",
  keywords =     "genetic algorithms, genetic programming",
  month =        "6-10 " # jul,
  organisation = "SIGEVO",
  address =      "Amsterdam, The Netherlands",
  DOI =          "doi:10.1145/2463372.2463483",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "We propose a novel crossover operator for tree-based
                 genetic programming, that produces approximately
                 geometric offspring. We empirically analyse certain
                 aspects of geometry of crossover operators and verify
                 performance of the new operator on both, training and
                 test fitness cases coming from set of symbolic
                 regression benchmarks. The operator shows superior
                 performance and higher probability of producing
                 geometric offspring than tree-swapping crossover and
                 other semantic-aware control methods.",
  notes =        "GSGP.

                 'That is as geometric as possible' p942.

                 p943 targets ideal mixture of parents (rather than
                 targeting the fitness function)

                 AGX Approximate matching (KD-tree, hashed) to
                 exponentially large library of all possible (small)
                 subtrees Exploit reversibility of common GP functions.
                 Keeps multiple possible reverses but bounds are placed
                 on potential exponential growth with crossover point
                 distance from root node (ie depth).

                 p944 'no crossover at depth 17 was geometric'

                 Mutation required. Different types of selection.

                 Also known as \cite{2463483} GECCO-2013 A joint meeting
                 of the twenty second international conference on
                 genetic algorithms (ICGA-2013) and the eighteenth
                 annual genetic programming conference (GP-2013)",
}

Genetic Programming entries for Krzysztof Krawiec Tomasz Pawlak

Citations