Additive Cellular Automata Augmented with Deep Learning for Pattern Reorganization

Abstract

This article also a new approach to classify several problems based on the properties of Additive Cellular Automata. We use a state-transition which consists of a set of disjoint trees rooted at cyclic states of unit cycle length thus forming a natural classifier. The framework proposed is strengthened with genetic algorithm to find the desired local rule of the modeling as a global state function