Analysis of information gain and Kolmogorov complexity for structural evaluation of cellular automata configurations. Issue 2 (2nd April 2016)

• Record Type: Journal Article
• Authors: Ali Javaheri Javid, Mohammad; Blackwell, Tim; Zimmer, Robert; Majid al-Rifaie, Mohammad
• Is Part Of: Connection science. Volume 28:Issue 2(2016)
• Page Start: 155
• Page End: 170
• Publication Date: 2016-04-02
• Subjects: Complexity -- entropy -- information gain -- Kolmogorov complexity -- computationalaesthetics -- cellular automata; Neural computers -- Periodicals; Artificial intelligence -- Periodicals; Cognitive science -- Periodicals; Connectionism -- Periodicals; 006.3
• DOI: 10.1080/09540091.2016.1151861
• Languages: English
• ISSNs: 0954-0091

Abstract

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.