GEESE: grammatical evolution algorithm for evolution of swarm behaviors

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@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