Artificial chemistry approach to exploring search spaces using Artificial Reaction Network agents

Claire E. Gerrard; John McCall; Christopher MacLeod; George M. Coghill

doi:10.1109/CEC.2013.6557702

Artificial chemistry approach to exploring search spaces using Artificial Reaction Network agents

Claire E. Gerrard, John McCall, Christopher MacLeod, George M. Coghill

Robert Gordon University

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

1 Citation (Scopus)

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.

Original language	English
Title of host publication	2013 IEEE Congress on Evolutionary Computation (CEC)
Publisher	IEEE Explore
Pages	1201-1208
Number of pages	8
ISBN (Electronic)	9781479904549, 9781479904525
ISBN (Print)	9781479904532
DOIs	https://doi.org/10.1109/CEC.2013.6557702
Publication status	Published - Jul 2013
Event	2013 IEEE Congress on Evolutionary Computation, CEC 2013 - Cancun, Mexico Duration: 20 Jun 2013 → 23 Jun 2013

Conference

Conference	2013 IEEE Congress on Evolutionary Computation, CEC 2013
Country/Territory	Mexico
City	Cancun
Period	20/06/13 → 23/06/13

Keywords

Artificial Chemistry
Artificial Reaction Networks
Swarm Robotics

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10.1109/CEC.2013.6557702Licence: Unspecified

Cite this

Gerrard, CE, McCall, J, MacLeod, C & Coghill, GM 2013, Artificial chemistry approach to exploring search spaces using Artificial Reaction Network agents. in 2013 IEEE Congress on Evolutionary Computation (CEC)., 6557702, IEEE Explore, pp. 1201-1208, 2013 IEEE Congress on Evolutionary Computation, CEC 2013, Cancun, Mexico, 20/06/13. https://doi.org/10.1109/CEC.2013.6557702

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title = "Artificial chemistry approach to exploring search spaces using Artificial Reaction Network agents",

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.",

keywords = "Artificial Chemistry, Artificial Reaction Networks, Swarm Robotics",

author = "Gerrard, {Claire E.} and John McCall and Christopher MacLeod and Coghill, {George M.}",

year = "2013",

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doi = "10.1109/CEC.2013.6557702",

language = "English",

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AU - McCall, John

AU - MacLeod, Christopher

AU - Coghill, George M.

PY - 2013/7

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N2 - 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.

AB - 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.

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KW - Artificial Reaction Networks

KW - Swarm Robotics

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ER -

Artificial chemistry approach to exploring search spaces using Artificial Reaction Network agents

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