- 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
Broken Jan 2021 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)",
