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 λ0 and 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.
","abstract_html":"<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 λ<sub>0 </sub>and λ<sub>1</sub> with their inherent primitives p<sub>0</sub> and p<sub>1</sub> 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>","abstract_has_math":false,"creators":["Huffman, Jordon"],"institution":null,"degree_name":"Computer Science - Applied Computing Track","degree_level":"Thesis","degree_discipline":"TSYS School of Computer Science","degree_department":null,"school":null,"contributors":["Rodrigo Obando","Rania Hodhod","Eugene Ionascu"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-05-01T07:00:00Z","date_published":"2016-05-01T07:00:00Z","updated_at":"2026-07-24T01:44:55Z","subjects":["John Von Neumann","Cellular Automata","Complex Systems","Similarity Measure","Computer Engineering","Computer Sciences","Theory and Algorithms"],"languages":["English"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://csuepress.columbusstate.edu/theses_dissertations/243","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Rodrigo Obando","Rania Hodhod","Eugene Ionascu"]},{"key":"dc:creator","label":"Author","values":["Huffman, Jordon"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2017-07-25T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["TSYS School of Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Computer Science - Applied Computing Track"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["John Von Neumann","Cellular Automata","Complex Systems","Similarity Measure","Computer Engineering","Computer Sciences","Theory and Algorithms"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://csuepress.columbusstate.edu/theses_dissertations/243"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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 λ0 and 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.
"]},{"key":"dc:title","label":"Title","values":["An Exploration of Rule Clustering in Cellular Automata Rule Spaces"]}]}],"canonical_facts":{"dc:contributor":["Rodrigo Obando","Rania Hodhod","Eugene Ionascu"],"dc:creator":["Huffman, Jordon"],"dc:date.available":["2017-07-25T07:00:00Z"],"dc:description.abstract":["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 λ0 and 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.
"],"dc:identifier":["https://csuepress.columbusstate.edu/theses_dissertations/243"],"dc:language":["English"],"dc:subject":["John Von Neumann","Cellular Automata","Complex Systems","Similarity Measure","Computer Engineering","Computer Sciences","Theory and Algorithms"],"dc:title":["An Exploration of Rule Clustering in Cellular Automata Rule Spaces"],"thesis:degree_discipline":["TSYS School of Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Computer Science - Applied Computing Track"]},"updated_at":"2026-07-24T01:44:55Z"}