Runtime analysis of mutation-based geometric semantic genetic programming for basis functions regression

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

@InProceedings{Moraglio:2013:GECCO,
  author =       "Alberto Moraglio and Andrea Mambrini",
  title =        "Runtime analysis of mutation-based geometric semantic
                 genetic programming for basis functions regression",
  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 =        "989--996",
  keywords =     "genetic algorithms, genetic programming",
  month =        "6-10 " # jul,
  organisation = "SIGEVO",
  address =      "Amsterdam, The Netherlands",
  DOI =          "doi:10.1145/2463372.2463492",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Geometric Semantic Genetic Programming (GSGP) is a
                 recently introduced form of Genetic Programming (GP)
                 that searches the semantic space of functions/programs.
                 The fitness landscape seen by GSGP is always, for any
                 domain and for any problem, unimodal with a linear
                 slope by construction. This makes the search for the
                 optimum much easier than for traditional GP, and it
                 opens the way to analyse theoretically in a easy manner
                 the optimisation time of GSGP in a general setting.
                 Very recent work proposed a runtime analysis of
                 mutation-based GSGP on the class of all Boolean
                 functions. We present a runtime analysis of
                 mutation-based GSGP on the class of all regression
                 problems with generic basis functions (encompassing
                 e.g., polynomial regression and trigonometric
                 regression).",
  notes =        "Also known as \cite{2463492} 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 Alberto Moraglio Andrea Mambrini

Citations