This paper addresses aesthetic problem in cellular automata, taking a quantitative approach
for aesthetic evaluation. Although the Shannon’s entropy is dominant in computational
methods of aesthetics, it fails to discriminate accurately structurally different patterns in
two-dimensions. We have adapted information gain measure and Kolmogorov complexity to
overcome the shortcomings of entropic measures. The measures are customised to robustly
quantify the complexity of multi-state cellular automata configurations.
Experiments are set up with different initial configurations in a two-dimensional multi-state
cellular automaton whose corresponding structural and Kolmogorov complexity measures at
global level are analysed. Preliminary outcomes on the resulting automata are promising,
as they suggest the possibility of predicting the structural characteristics, symmetry and
orientation of cellular automata generated patterns.