- 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 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 = "17--24",
- month = "6-10 " # jul,
- organisation = "SIGEVO",
- address = "Amsterdam, The Netherlands",
- keywords = "genetic algorithms, genetic programming, grammatical evolution",
-
URL = "
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.309.3738",
-
URL = "
http://bio.kuleuven.be/ento/pdfs/ferrante_etal_gecco_2013.pdf",
-
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)",
