Javaheri Javid, Mohammad Ali, Blackwell, Tim, Zimmer, Robert and Al-Rifaie, Mohammad Majid ORCID: https://orcid.org/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: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 / 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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