Automatic Synthesis of Swarm Behavioural Rules from their Atomic Components

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

@InProceedings{Samarasinghe:2018:GECCO,
  author =       "Dilini Samarasinghe and Erandi Lakshika and 
                 Michael Barlow and Kathryn Kasmarik",
  title =        "Automatic Synthesis of Swarm Behavioural Rules from
                 their Atomic Components",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference, GECCO 2018",
  year =         "2018",
  isbn13 =       "978-1-4503-5618-3",
  address =      "Japan",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "15-19 " # jul,
  keywords =     "genetic algorithms, genetic programming, Grammatical
                 Evolution, Multi-agent Systems, Genetic Programming,
                 Swarm Intelligence, Artificial Life",
  URL =          "http://www.human-competitive.org/sites/default/files/samarasinghe-paper.pdf",
  DOI =          "doi:10.1145/3205455.3205546",
  size =         "8 page",
  abstract =     "This paper presents an evolutionary computing based
                 approach to automatically synthesise swarm behavioural
                 rules from their atomic components, thus making a step
                 forward in trying to mitigate human bias from the rule
                 generation process, and leverage the full potential of
                 swarm systems in the real world by modelling more
                 complex behaviours. We identify four components that
                 make-up the structure of a rule: control structures,
                 parameters, logical/relational connectives and
                 preliminary actions, which form the rule space for the
                 proposed approach. A boids simulation system is
                 employed to evaluate the approach with grammatical
                 evolution and genetic programming techniques using the
                 rule space determined. While statistical analysis of
                 the results demonstrates that both methods successfully
                 evolve desired complex behaviours from their atomic
                 components, the grammatical evolution model shows more
                 potential in generating complex behaviours in a
                 modularised approach. Furthermore, an analysis of the
                 structure of the evolved rules implies that the genetic
                 programming approach only derives non-reusable rules
                 composed of a group of actions that is combined to
                 result in emergent behaviour. In contrast, the
                 grammatical evolution approach synthesises sound and
                 stable behavioural rules which can be extracted and
                 reused, hence making it applicable in complex
                 application domains where manual design is
                 infeasible.",
  notes =        "2018 HUMIES
                 finalist

                 http://gecco-2018.sigevo.org/index.html/tiki-index.php?page=Accepted+Papers

                 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 Dilini Samarasinghe Erandi Hene Kankanamge Michael Barlow Kathryn Kasmarik

Citations