An Exploration of Rule Clustering in Cellular Automata Rule Spaces

<p>The study of complex systems examines the global behavior of a system and how the individual parts of the system affect that behavior [1]. The study of complex systems spans across many fields of science like biology, physics, engineering, and computer science. One area of complex systems that has not been fully explored is cellular automata. Since its discovery by John von Neumann, there have been no consistent ways of categorizing similarities between cellular automata rules or collecting similar rules for observation. This thesis introduces an approach to identifying clusters of similar rules and extracting rules from that cluster. Several similarity measures were developed to establish similarity between rules. All similarity measure approaches are outlined in this thesis, but only one was selected for determining similarity in this approach. Based on a partitioning of the rule space, this approach uses λ₀and λ₁ with their inherent primitives p₀ and p₁ to obtain a cluster identification string [5], The cluster Id. is determined by the output of the surrounding neighbors of any rule in the cluster. This cluster Id. can be used to produce a set of rules, all yielding the same or similar output.</p>

Name thesis:degree_name

Computer Science - Applied Computing Track

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

TSYS School of Computer Science

Year dc:date.available

2016

Author dc:creator

• Huffman, Jordon

Contributors dc:contributor

• Rodrigo Obando
• Rania Hodhod
• Eugene Ionascu

• John Von Neumann
• Cellular Automata
• Complex Systems
• Similarity Measure
• Computer Engineering
• Computer Sciences
• Theory and Algorithms

Language dc:language

English

Repository record dc:identifier

https://csuepress.columbusstate.edu/theses_dissertations/243

OAI identifier oai:identifier

oai:csuepress.columbusstate.edu:theses_dissertations-1234