Identification of cellular automata based on incomplete observations with bounded time gaps

In this paper, the problem of identifying the cellular automata (CAs) is considered. We frame and solve this problem in the context of incomplete observations, i.e., prerecorded, incomplete configurations of the system at certain, and unknown time stamps. We consider 1-D, deterministic, two-state CAs only. An identification method based on a genetic algorithm with individuals of variable length is proposed. The experimental results show that the proposed method is highly effective. In addition, connections between the dynamical properties of CAs (Lyapunov exponents and behavioral classes) and the performance of the identification algorithm are established and analyzed

A statistical approach to the identification of diploid cellular automata based on incomplete observations

In this paper, the identification problem of diploid cellular automata is considered, in which, based on a series of incomplete observations, the underlying cellular automaton rules and the states of missing cell states are to be uncovered. An algorithm for identifying the rule, based on a statistical parameter estimation method using a normal distribution approximation, is presented. In addition, an algorithm for filling the missing cell states is formulated. The accuracy of these methods is examined in a series of computational experiments

Investigation on cellular automaton rule estimation

In this article is investigate possibility of evolutionary algorithms use on one-dimensional cellular automaton rule estimation. Selected evolutionary algorithms are used in order to estimate rules that are used in cellular automaton like rule 30, 90 etc. Selected evolutionary algorithms are used as for example differential evolution or SOMA algorithms. Results are mutually compared and reported in the article. The main aim is to use techniques proposed here on more complex structures and automatons, as discussed in the conclusion.Web of Science13432330