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 |
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Uncontrolled Keywords: | Complexity, entropy, information gain, Kolmogorov complexity, computational aesthetics, cellular automata |
Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
Faculty / School / Research Centre / Research Group: | Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS) Faculty of Engineering & Science |
Last Modified: | 04 Mar 2022 13:07 |
URI: | http://gala.gre.ac.uk/id/eprint/24751 |
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