Javaheri Javid, Mohammad Ali; Blackwell, Tim; Zimmer, Robert and al-Rifaie, Mohammad Majid. 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 [Article]

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Abstract or Description

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
Identification Number (DOI):	https://doi.org/10.1080/09540091.2016.1151861
Keywords:	Complexity, entropy, information gain, Kolmogorov complexity, computationalaesthetics, cellular automata
Departments, Centres and Research Units:	Computing
Dates:	Date Event 4 February 2016 Accepted 9 March 2016 Published
Item ID:	17227
Date Deposited:	18 Mar 2016 15:19
Last Modified:	31 Oct 2024 16:55
Peer Reviewed:	Yes, this version has been peer-reviewed.
URI:	https://research.gold.ac.uk/id/eprint/17227

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