Self-adaptive mate choice for cluster geometry optimization

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Leitao:2013:GECCO,
  author =       "Antonio Leitao and Penousal Machado",
  title =        "Self-adaptive mate choice for cluster geometry
                 optimization",
  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 =        "957--964",
  keywords =     "genetic algorithms, genetic programming",
  month =        "6-10 " # jul,
  organisation = "SIGEVO",
  address =      "Amsterdam, The Netherlands",
  DOI =          "doi:10.1145/2463372.2463494",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Sexual Selection through Mate Choice has, over the
                 past few decades, attracted the attention of
                 researchers from various fields. They have gathered
                 numerous supporting evidence, establishing Mate Choice
                 as a major driving force of evolution, capable of
                 shaping complex traits and behaviours. Despite its wide
                 acceptance and relevance across various research
                 fields, the impact of Mate Choice in Evolutionary
                 Computation is still far from understood, both
                 regarding performance and behaviour.

                 In this study we describe a nature-inspired
                 self-adaptive mate choice model, relying on a Genetic
                 Programming representation tailored for the
                 optimisation of Morse clusters, a relevant and widely
                 accepted problem for benchmarking new algorithms, which
                 provides a set of hard test instances. The model is
                 coupled with a state-of-the-art hybrid steady-state
                 approach and both its performance and behaviour are
                 assessed with a particular interest on the replacement
                 strategy's acceptance rate and diversity handling.",
  notes =        "Also known as \cite{2463494} 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 Antonio Leitao Penousal Machado

Citations