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

Javid Javaheri, Mohammad Ali; Blackwell, Tim M.; 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:

DateEvent
4 February 2016Accepted
9 March 2016Published

Item ID:

17227

Date Deposited:

18 Mar 2016 15:19

Last Modified:

27 Feb 2019 10:32

Peer Reviewed:

Yes, this version has been peer-reviewed.

URI:

http://research.gold.ac.uk/id/eprint/17227

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