Fast Rule Identification and Neighborhood Selection for Cellular Automata

School of Computer Science and Informatics, Cardiff University, Cardiff, UK.
IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics: a publication of the IEEE Systems, Man, and Cybernetics Society (Impact Factor: 6.22). 12/2010; 41(3):749-60. DOI: 10.1109/TSMCB.2010.2091271
Source: PubMed


Cellular automata (CA) with given evolution rules have been widely investigated, but the inverse problem of extracting CA rules from observed data is less studied. Current CA rule extraction approaches are both time consuming and inefficient when selecting neighborhoods. We give a novel approach to identifying CA rules from observed data and selecting CA neighborhoods based on the identified CA model. Our identification algorithm uses a model linear in its parameters and gives a unified framework for representing the identification problem for both deterministic and probabilistic CA. Parameters are estimated based on a minimum variance criterion. An incremental procedure is applied during CA identification to select an initial coarse neighborhood. Redundant cells in the neighborhood are then removed based on parameter estimates, and the neighborhood size is determined using the Bayesian information criterion. Experimental results show the effectiveness of our algorithm and that it outperforms other leading CA identification algorithms.

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Available from: Xianfang Sun
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    • "Most of the developed algorithms for neighborhood detection of CA adopt the coarse-to-fine approach, which detects an initial neighborhood first and then refines it by choosing significant neighbors [9] or removing redundant neighbors [16] from the initial neighborhood . Sun et al. [16] developed an approach to determine the initial neighborhood, which calculates the variance estimation by varying the neighborhood size, until the variance is smaller than criterion σ "
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