Automatic Entropy-based Classification of Cellular Automata
Eviatar Bach
Ideal Mini School
Floor Location : S 204 P

Cellular automata are discrete, dynamical systems that are studied because of their self-organizational and computational properties. Various forms of classification have emerged in recent years, both qualitative and quantitative. This project presents a novel approach for classifying these systems by measuring the evolution of Shannon's entropy in distinct rules in the k=2, r=1 rulespace. It provides an advantage over previous methods because it is a rigorous, mathematical approach, can be performed automatically using computers, and is applicable to more complex systems.