Cellular automata rule detection using circular asynchronous evolutionary search

Abstract

A circular evolutionary model is proposed to produce Cellular Automata (CA) rules for the computationally emergent task of density classification. The task refers to determining the initial density most present in the initial cellular state of a one-dimensional cellular automaton within a number of update steps. This is a challenging problem extensively studied due to its simplicity and potential to generate a variety of complex behaviors. The proposed circular evolutionary model aims to facilitate a good exploitation of relevant genetic material while increasing the population diversity. This goal is achieved by integrating a fitness guided population topology with an asynchronous search scheme. Both selection and recombination take place asynchronously enabling a gradual propagation of information from the fittest individuals towards the less fit members of the population. Numerical experiments emphasize a competitive performance of the circular search algorithm compared to other evolutionary models indicating the potential of the proposed model. © 2009 Springer Berlin Heidelberg.