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Analysis of information gain and Kolmogorov complexity for structural evaluation of cellular automata configurations

Analysis of information gain and Kolmogorov complexity for structural evaluation of cellular automata configurations

Javaheri Javid, Mohammad Ali, Blackwell, Tim, Zimmer, Robert and Al-Rifaie, Mohammad Majid ORCID: 0000-0002-1798-9615 (2016) Analysis of information gain and Kolmogorov complexity for structural evaluation of cellular automata configurations. Connection Science, 28 (2). pp. 155-170. ISSN 0954-0091 (Print), 1360-0494 (Online) (doi:https://doi.org/10.1080/09540091.2016.1151861)

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Abstract

Shannon entropy fails to discriminate structurally different patterns in two-dimensional images. We have adapted information gain measure and Kolmogorov complexity to overcome the shortcomings of entropy as a measure of image structure. The measures are customised to robustly quantify the complexity of images resulting from multi-state cellular automata (CA). Experiments with a two-dimensional multi-state cellular automaton demonstrate that these measures are able to predict some of the structural characteristics, symmetry and orientation of CA generated patterns.

Item Type: Article
Uncontrolled Keywords: Complexity, entropy, information gain, Kolmogorov complexity, computational aesthetics, cellular automata
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / Department / Research Group: Faculty of Architecture, Computing & Humanities
Faculty of Architecture, Computing & Humanities > Department of Computing & Information Systems
Last Modified: 12 Jul 2019 12:33
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/24751

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