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

An evolutionary approach to the identification of Cellular Automata based on partial observations

In this paper we consider the identification problem of Cellular Automata (CAs). The problem is defined and solved in the context of partial observations with time gaps of unknown length, i.e. pre-recorded, partial configurations of the system at certain, unknown time steps. A solution method based on a modified variant of a Genetic Algorithm (GA) is proposed and illustrated with brief experimental results

Affine continuous cellular automata solving the fixed-length density classification problem

In this paper, the classical density classification problem is considered in the context of affine continuous cellular automata. It has been shown earlier that there exists no general solution to this problem that is valid for any number of cells. Here, we consider this problem in the case of a fixed number of cells. Necessary conditions for solving the problem are formulated. Based on this knowledge, a specific class of affine continuous cellular automata is evaluated experimentally for 23 cells. A rich solution set is analysed and visualised

Two-dimensional affine continuous cellular automata solving the relaxed density classification problem

The density classification problem is one of the most studied problems in the context of the computational abilities of cellular automata. Since this problem cannot be solved in the classical sense, we consider a weaker version, by slightly relaxing the assumptions on the output specification. In this paper, we discuss this relaxed problem for two-dimensional Affine Continuous Cellular Automata (ACCAs). We focus on finding the most performant rules solving this problem among the density-conserving ones by evaluating ACCAs experimentally for a predefined set of initial configurations