Computational complexity analysis of multi-objective genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  author =       "Frank Neumann",
  title =        "Computational complexity analysis of multi-objective
                 genetic programming",
  booktitle =    "GECCO '12: Proceedings of the fourteenth international
                 conference on Genetic and evolutionary computation
  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 =        "799--806",
  keywords =     "genetic algorithms, genetic programming",
  month =        "7-11 " # jul,
  organisation = "SIGEVO",
  address =      "Philadelphia, Pennsylvania, USA",
  DOI =          "doi:10.1145/2330163.2330274",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "The computational complexity analysis of genetic
                 programming (GP) has been started recently in [7] by
                 analyzing simple (1+1) GP algorithms for the problems
                 ORDER and MAJORITY. In this paper, we study how taking
                 the complexity as an additional criteria influences the
                 runtime behavior. We consider generalizations of ORDER
                 and MAJORITY and present a computational complexity
                 analysis of (1+1) GP using multi-criteria fitness
                 functions that take into account the original objective
                 and the complexity of a syntax tree as a secondary
                 measure. Furthermore, we study the expected time until
                 simple multi-objective genetic programming algorithms
                 have computed the Pareto front when taking the
                 complexity of a syntax tree as an equally important
  notes =        "See also \cite{Nguyen:2013:foga}. Also known as
                 \cite{2330274} 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 Frank Neumann