A Fast Method for Classification of Emergent Dynamics in Cellular Automata Based on Uncertainty Profiles

A new and efficient method to classify cellular automata is presented and exemplified here for the case of elementary cellular automata (ECA) with 3 cells neighborhood. The approach has an important advantages over other methods: It is extremely fast since it does not require the simulation of the cellular automaton dynamics, instead all classification process is based on a uncertainty profile computed rapidly for a given cell logic and neighborhood. The method may be easily generalized to more complex cellular neighborhoods. A comparison with another recent ECA classification method (based on a completely different approach, namely on the iterated maps theory in nonlinear dynamics) reveals a strong overlap between results.