Analysis of Information Gain and Kolmogorov Complexity for Structural Evaluation of Cellular Automata Configurations

Javaheri Javid, M. A., Tim Blackwell, B., Robert, Z. and Mohammad Majid, a.-R. (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

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[thumbnail of © 2016 Taylor & Francis.This article is published by Taylor & Francis in Connection Science on 9 March 2016 available online: https://doi.org/10.1080/09540091.2016.1151861] Text (© 2016 Taylor & Francis.This article is published by Taylor & Francis in Connection Science on 9 March 2016 available online: 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: Articles
Additional Information: © 2016 Taylor & Francis
Uncontrolled Keywords: Complexity, entropy, information gain, Kolmogorov complexity, computationalaesthetics, cellular automata
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Academic Areas > Business School
Depositing User: Mohammad Ali Javaheri Javid
Date Deposited: 26 Apr 2023 11:09
Last Modified: 26 Apr 2023 11:09
URI: https://eprints_test.chi.ac.uk/id/eprint/6794

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