Analysis of information gain and Kolmogorov complexity for structural evaluation of cellular automata configurations. Issue 2 (2nd April 2016) - Ali Javaheri Javid, Mohammad; Blackwell, Tim; Zimmer, Robert; Majid al-Rifaie, Mohammad
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.