Morphogenetic Engineering For Evolving Ant Colony Pheromone Communication

Vaughan, Neil

dc.contributor.author	Vaughan, Neil	*
dc.date.accessioned	2018-03-23T15:54:45Z
dc.date.available	2018-03-23T15:54:45Z
dc.date.issued	2018-04-06
dc.identifier.citation	Vaughan, N. (2018). Morphogenetic engineering For evolving ant colony pheromone communication In Annual Convention of the Society for the Study of Artificial Intelligence and the Simulation of Behaviour (AISB 2018): Proceedings of the AISB2018 conference, 4-6 April 2018, University of Liverpool (pp. 218-325). Bath, United Kingdom: Society for the Study of Artificial Intelligence and Simulation for Behaviour.	en
dc.identifier.isbn	9781510872769
dc.identifier.uri	http://hdl.handle.net/10034/621023
dc.description.abstract	This research investigates methods for evolving swarm communication in a simulated colony of ants using pheromone when foriaging for food. This research implemented neuroevolution and obtained the capability to learn pheromone communication autonomously. Building on previous literature on pheromone communication, this research applies evolution to adjust the topology and weights of an artificial neural network which controls the ant behaviour. Comparison of performance is made between a hard-coded benchmark algorithm, a fixed topology ANN and neuroevolution of the ANN topology and weights. The resulting neuroevolution produced a neural network which was successfully evolved to achieve the task objective, to collect food and return it to the nest.
dc.language.iso	en	en
dc.publisher	The Society for the Study of Artificial Intelligence and Simulation for Behaviour (AISB)	en
dc.relation.url	https://web.archive.org/web/20191122163138/http://aisb2018.csc.liv.ac.uk/index.html
dc.relation.url	http://toc.proceedings.com/41519webtoc.pdf
dc.relation.url	http://www.proceedings.com/41519.html
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/4.0/	en
dc.subject	Ant Colony	en
dc.subject	Evolutionary algorithms	en
dc.subject	Neuroevolution	en
dc.subject	artificial intelligence	en
dc.title	Morphogenetic Engineering For Evolving Ant Colony Pheromone Communication	en
dc.type	Meetings and Proceedings	en
dc.contributor.department	University of Chester	en
dc.date.accepted	2018-02-01
or.grant.openaccess	Yes	en
rioxxterms.funder	Royal Academy of Engineering	en
rioxxterms.identifier.project	CSIS17/03	en
rioxxterms.version	AM	en
rioxxterms.licenseref.startdate	2018-04-06
html.description.abstract	This research investigates methods for evolving swarm communication in a simulated colony of ants using pheromone when foriaging for food. This research implemented neuroevolution and obtained the capability to learn pheromone communication autonomously. Building on previous literature on pheromone communication, this research applies evolution to adjust the topology and weights of an artificial neural network which controls the ant behaviour. Comparison of performance is made between a hard-coded benchmark algorithm, a fixed topology ANN and neuroevolution of the ANN topology and weights. The resulting neuroevolution produced a neural network which was successfully evolved to achieve the task objective, to collect food and return it to the nest.