An evolutionary approach to the identification of Cellular Automata based on partial observations
This addresses the challenge of reconstructing CA rules from incomplete data, which is incremental as it builds on existing evolutionary approaches.
The paper tackled the problem of identifying Cellular Automata from partial and temporally sparse observations, proposing a modified Genetic Algorithm method and demonstrating it with brief experimental results.
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.