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

TL;DR

This paper presents an evolutionary method using a modified Genetic Algorithm to identify Cellular Automata from partial, incomplete observations with unknown time gaps, advancing the understanding of CA dynamics from limited data.

Contribution

It introduces a novel GA-based approach for CA identification using partial observations with unknown time gaps, addressing a challenging problem in the field.

Findings

The method successfully identifies CA rules from incomplete data.

Experimental results demonstrate the approach's effectiveness.

The approach handles unknown time gaps in observations.

Abstract

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.