PROPERTIES OF SELF-REPLICATING CELLULAR AUTOMATA SYSTEMS DISCOVERED USING GENETIC PROGRAMMING

Zhijian Pan (), James Reggia () and Donghong Gao ()
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Zhijian Pan: IBM Annapolis Lab, 1997 Annapolis Exchange Parkway, Annapolis, MD 21401, USA
James Reggia: University of Maryland, Computer Science Department, A. V. Williams Building, College Park, MD 20742, USA
Donghong Gao: Telecommunication Systems, 275 West Street, Annapolis, MD 21401, USA

Advances in Complex Systems (ACS), 2007, vol. 10, issue supp0, 61-84

Abstract: We recently formulated an approach to representing structures in cellular automata (CA) spaces, and the rules that govern cell state changes, that is amenable to manipulation by genetic programming (GP). Using this approach, it is possible to efficiently generate self-replicating configurations for fairly arbitrary initial structures. Here, we investigate the properties of self-replicating systems produced using GP in this fashion as the initial configuration's size, shape, symmetry, allowable states, and other factors are systematically varied. We find that the number of GP generations, computation time, and number of resulting rules required by an arbitrary structure to self-replicate are positively and jointly correlated with the number of components, configuration shape, and allowable states in the initial configuration, but inversely correlated with the presence of repeated components, repeated sub-structures, and/or symmetric sub-structures. We conclude that GP can be used as a "replicator factory" to produce a wide range of self-replicating CA configurations, and that the properties of the resulting replicators can be predicted in parta priori. The rules controlling self-replication that are created by GP generally differ from those created manually in past CA studies.

Keywords: Artificial life; self-replication; cellular automata; evolutionary computing (search for similar items in EconPapers)
Date: 2007
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DOI: 10.1142/S0219525907001112

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