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Information gain measure for structural discrimination of cellular automata configurations

Information gain measure for structural discrimination of cellular automata configurations

Javaheri Javid, Mohammad Ali, Blackwell, Tim, Zimmer, Robert and Al-Rifaie, Mohammad Majid ORCID: 0000-0002-1798-9615 (2015) Information gain measure for structural discrimination of cellular automata configurations. In: 2015 7th Computer Science and Electronic Engineering Conference (CEEC). Institute of Electrical and Electronics Engineers (IEEE), Colchester, UK, pp. 47-52. ISBN 978-1467394819 (doi:https://doi.org/10.1109/CEEC.2015.7332698)

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Abstract

Cellular automata (CA) are known for their capability in exhibiting interesting emergent behaviour and capacity to generate complex and often aesthetically appealing patterns through the local interaction of rules. Mean information gain has been suggested as a measure of discriminating structurally different two-dimensional (2D) patterns. This paper addresses quantitative evaluation of the complexity of CA generated configurations. In particular, we examine information gain as a spatial complexity measure for discriminating multi-state 2D CA generated configurations. This information-theoretic quantity, also known as conditional entropy, takes into account conditional and joint probabilities of cell states in a 2D plane. The effectiveness of the measure is shown in a series of experiments for multi-state 2D patterns generated by CA. The results of the experiments show that the measure is capable of distinguishing the structural characteristics including symmetries and randomness of 2D CA patterns.

Item Type: Conference Proceedings
Title of Proceedings: 2015 7th Computer Science and Electronic Engineering Conference (CEEC)
Uncontrolled Keywords: Complexity theory, entropy, automata, gain measurement, uncertainty, atmospheric measurements, particle measurements
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / Department / Research Group: Faculty of Liberal Arts & Sciences
Faculty of Liberal Arts & Sciences > Centre for Computer & Computational Science
Faculty of Liberal Arts & Sciences > School of Computing & Mathematical Sciences (CAM)
Last Modified: 20 Jul 2021 09:30
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None
URI: http://gala.gre.ac.uk/id/eprint/21004

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