GESwarm: grammatical evolution for the automatic synthesis of collective behaviors in swarm robotics

Created by W.Langdon from gp-bibliography.bib Revision:1.4333

  author =       "Eliseo Ferrante and Edgar Duenez-Guzman and 
                 Ali Emre Turgut and Tom Wenseleers",
  title =        "{GESwarm}: grammatical evolution for the automatic
                 synthesis of collective behaviors in swarm robotics",
  booktitle =    "GECCO '13: Proceeding of the fifteenth annual
                 conference on Genetic and evolutionary computation
  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 =        "17--24",
  month =        "6-10 " # jul,
  organisation = "SIGEVO",
  address =      "Amsterdam, The Netherlands",
  keywords =     "genetic algorithms, genetic programming, grammatical
  URL =          "",
  URL =          "",
  DOI =          "doi:10.1145/2463372.2463385",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  size =         "8 pages",
  abstract =     "In this paper we propose GESwarm, a novel tool that
                 can automatically synthesise collective behaviours for
                 swarms of autonomous robots through evolutionary
                 robotics. Evolutionary robotics typically relies on
                 artificial evolution for tuning the weights of an
                 artificial neural network that is then used as
                 individual behaviour representation. The main caveat of
                 neural networks is that they are very difficult to
                 reverse engineer, meaning that once a suitable solution
                 is found, it is very difficult to analyse, to modify,
                 and to tease apart the inherent principles that lead to
                 the desired collective behaviour. In contrast, our
                 representation is based on completely readable and
                 analysable individual-level rules that lead to a
                 desired collective behaviour.

                 The core of our method is a grammar that can generate a
                 rich variety of collective behaviours. We test GESwarm
                 by evolving a foraging strategy using a realistic swarm
                 robotics simulator. We then systematically compare the
                 evolved collective behaviour against an hand-coded one
                 for performance, scalability and flexibility, showing
                 that collective behaviours evolved with GESwarm can
                 outperform the hand-coded one.",
  notes =        "Also known as \cite{2463385} 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 Eliseo Ferrante Edgar Duenez-Guzman Ali Emre Turgut Tom Wenseleers