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

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.