Deterministic Computing Mechanism for Perfect Density Classification

Abstract

The purpose of the present study is to search one-dimensional Cellular Automata (CA) rules which will solve the density classification task (DCT) perfectly. The mathematical analysis of number conserving functions over binary strings of length n gives an indication of its corresponding number conserving cellular automata rules (either uniform or non-uniform). The state transition diagrams (STDs) of number conserving CA rules have been analyzed where it has been found that these STDs can generate different DCT solutions. While studying the properties of STDs, an interesting classification of binary strings could be made where equal weight strings form a class and the cardinality of each class is same as the binomial coefficient nCk; n is the length and k is the weight of the binary string. Apart from STDs, other deterministic methods have been proposed to obtain the exact solution of DCT. All these exact solutions of DCT using different deterministic methods can be viewed as an improvement over the soft computing techniques used earlier to obtain approximate solutions.

19 pages, 5 figures and one table

References in corpus (4)

• Solution of the Density Classification Problem with Two Cellular Automata Rules
• Cellular automaton rules conserving the number of active sites
• One Dimensional $n$ary Density Classification Using Two Cellular Automaton Rules
• Solutions on 1D and 2D Density Classification Problem Using Programmable Cellular Automata