Artificial chemistry approach to exploring search spaces using artificial reaction network agents.

Gerrard, Claire E.; McCall, John; Macleod, Christopher; Coghill, George M.

Abstract

The Artificial Reaction Network (ARN) is a cell signaling network inspired representation belonging to the branch of A-Life known as Artificial Chemistry. It has properties in common with both AI and Systems Biology techniques including Artificial Neural Networks, Petri Nets, Random Boolean Networks and S-Systems. The ARN has been previously applied to control of limbed robots and simulation of biological signaling pathways. In this paper, multiple instances of independent distributed ARN controlled agents function to find the global minima within a set of simulated environments characterized by benchmark problems. The search behavior results from the internal ARN network, but is enhanced by collective activities and stigmergic interaction of the agents. The results show that the agents are able to find best fitness solutions in all problems, and compare well with results of cell inspired optimization algorithms. Such a system may have practical application in distributed or swarm robotics.

Citation

GERRARD, C.E., MCCALL, J., MACLEOD, C. and COGHILL, G.M. 2013. Artificial chemistry approach to exploring search spaces using artificial reaction network agents. In Proceedings of the 2013 IEEE congress on evolutionary computation (CEC 2013), 20-23 June 2013, Cancun, Mexico. New York: IEEE [online], article number 6557702, pages 1201-1208. Available from: https://doi.org/10.1109/CEC.2013.6557702

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2013 IEEE congress on evolutionary computation (CEC 2013)
• Start Date: Jun 20, 2013
• End Date: Jun 23, 2013
• Acceptance Date: Jun 23, 2013
• Online Publication Date: Jun 23, 2013
• Publication Date: Jul 15, 2013
• Deposit Date: Aug 12, 2013
• Publicly Available Date: Aug 12, 2013
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Article Number: 6557702
• Pages: 1201-1208
• Series Title: IEEE transactions on evolutionary computation
• ISBN: 9781479904549; 9781479904532; 9781479904518
• DOI: https://doi.org/10.1109/CEC.2013.6557702
• Keywords: Artificial reaction networks; Artificial chemistry; Swarm robotics
• Public URL: http://hdl.handle.net/10059/849
• Contract Date: Aug 12, 2013