GEESE: grammatical evolution algorithm for evolution of swarm behaviors

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@InProceedings{Neupane:2018:GECCO,
  author =       "Aadesh Neupane and Michael A. Goodrich and 
                 Eric G. Mercer",
  title =        "{GEESE}: grammatical evolution algorithm for evolution
                 of swarm behaviors",
  booktitle =    "GECCO '18: Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "2018",
  editor =       "Hernan Aguirre and Keiki Takadama and 
                 Hisashi Handa and Arnaud Liefooghe and Tomohiro Yoshikawa and 
                 Andrew M. Sutton and Satoshi Ono and Francisco Chicano and 
                 Shinichi Shirakawa and Zdenek Vasicek and 
                 Roderich Gross and Andries Engelbrecht and Emma Hart and 
                 Sebastian Risi and Ekart Aniko and Julian Togelius and 
                 Sebastien Verel and Christian Blum and Will Browne and 
                 Yusuke Nojima and Tea Tusar and Qingfu Zhang and 
                 Nikolaus Hansen and Jose Antonio Lozano and 
                 Dirk Thierens and Tian-Li Yu and Juergen Branke and 
                 Yaochu Jin and Sara Silva and Hitoshi Iba and 
                 Anna I Esparcia-Alcazar and Thomas Bartz-Beielstein and 
                 Federica Sarro and Giuliano Antoniol and Anne Auger and 
                 Per Kristian Lehre",
  isbn13 =       "978-1-4503-5618-3",
  pages =        "999--1006",
  address =      "Kyoto, Japan",
  DOI =          "doi:10.1145/3205455.3205619",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "15-19 " # jul,
  organisation = "SIGEVO",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution",
  abstract =     "Animals such as bees, ants, birds, fish, and others
                 are able to perform complex coordinated tasks like
                 foraging, nest-selection, flocking and escaping
                 predators efficiently without centralized control or
                 coordination. Conventionally, mimicking these
                 behaviours with robots requires researchers to study
                 actual behaviors, derive mathematical models, and
                 implement these models as algorithms. We propose a
                 distributed algorithm, Grammatical Evolution algorithm
                 for Evolution of Swarm behaviours (GEESE), which uses
                 genetic methods to generate collective behaviors for
                 robot swarms. GEESE uses grammatical evolution to
                 evolve a primitive set of human-provided rules into
                 productive individual behaviors. The GEESE algorithm is
                 evaluated in two different ways. First, GEESE is
                 compared to state-of-the-art genetic algorithms on the
                 canonical Santa Fe Trail problem. Results show that
                 GEESE outperforms the state-of-the-art by (a) providing
                 better solution quality given sufficient population
                 size while (b) using fewer evolutionary steps. Second,
                 GEESE outperforms both a hand-coded and a Grammatical
                 Evolution-generated solution on a collective swarm
                 foraging task.",
  notes =        "Also known as \cite{3205619} GECCO-2018 A
                 Recombination of the 27th International Conference on
                 Genetic Algorithms (ICGA-2018) and the 23rd Annual
                 Genetic Programming Conference (GP-2018)",
}

Genetic Programming entries for Aadesh Neupane Michael A Goodrich Eric G Mercer

Citations