Abstract
A new neighborhood selection method is presented for both deterministic and probabilistic cellular automata models. The detection criteria are built explicitly on the corresponding contribution which is made to the value of each updated cell from each detected cell in the evolution. Theoretical analysis and numerical simulations demonstrate the effectiveness of this new method.
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