The max problem revisited: the importance of mutation in genetic programming

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@InProceedings{Koetzing:2012:GECCO,
  author =       "Timo Koetzing and Andrew M. Sutton and 
                 Frank Neumann and Una-May O'Reilly",
  title =        "The max problem revisited: the importance of mutation
                 in genetic programming",
  booktitle =    "GECCO '12: Proceedings of the fourteenth international
                 conference on Genetic and evolutionary computation
                 conference",
  year =         "2012",
  editor =       "Terry Soule and Anne Auger and Jason Moore and 
                 David Pelta and Christine Solnon and Mike Preuss and 
                 Alan Dorin and Yew-Soon Ong and Christian Blum and 
                 Dario Landa Silva and Frank Neumann and Tina Yu and 
                 Aniko Ekart and Will Browne and Tim Kovacs and 
                 Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and 
                 Giovanni Squillero and Nicolas Bredeche and 
                 Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and 
                 Martin Pelikan and Silja Meyer-Nienberg and 
                 Christian Igel and Greg Hornby and Rene Doursat and 
                 Steve Gustafson and Gustavo Olague and Shin Yoo and 
                 John Clark and Gabriela Ochoa and Gisele Pappa and 
                 Fernando Lobo and Daniel Tauritz and Jurgen Branke and 
                 Kalyanmoy Deb",
  isbn13 =       "978-1-4503-1177-9",
  pages =        "1333--1340",
  keywords =     "genetic algorithms, genetic programming, theory",
  month =        "7-11 " # jul,
  organisation = "SIGEVO",
  address =      "Philadelphia, Pennsylvania, USA",
  DOI =          "doi:10.1145/2330163.2330348",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "This paper contributes to the rigorous understanding
                 of genetic programming algorithms by providing runtime
                 complexity analyses of the well-studied Max problem.
                 Several experimental studies have indicated that it is
                 hard to solve the Max problem with crossover-based
                 algorithms. Our analyses show that different variants
                 of the Max problem can provably be solved using simple
                 mutation-based genetic programming algorithms.

                 Our results advance the body of computational
                 complexity analyses of genetic programming, indicate
                 the importance of mutation in genetic programming, and
                 reveal new insights into the behavior of mutation-based
                 genetic programming algorithms.",
  notes =        "Also known as \cite{2330348} GECCO-2012 A joint
                 meeting of the twenty first international conference on
                 genetic algorithms (ICGA-2012) and the seventeenth
                 annual genetic programming conference (GP-2012)",
}

Genetic Programming entries for Timo Koetzing Andrew M Sutton Frank Neumann Una-May O'Reilly

Citations