Journal of Cellular Automata, Vol. 2, pp. 77–102 Reprints available directly from the publisher Photocopying permitted by license only  c 2007 Old City Publishing, Inc. Published by license under the OCP Science imprint, a member of the Old City Publishing Group An Evolutionary Methodology for the Automated Design of Cellular Automaton-based Complex Systems GERM ´ AN TERRAZAS,PETER SIEPMANN,GRAHAM KENDALL, AND NATALIO KRASNOGOR ASAP Group, School of Computer Science and IT, University of Nottingham, Jubilee Campus, Nottingham, NG8 1BB, United Kingdom E-mail: [gzt, pas, gxk, nxk]@cs.nott.ac.uk Received: September 29, 2006; Accepted: November 9, 2006 Cellular automata (CA) are an important modelling paradigm in the natural sciences and an extremely useful approach in the study of complex systems. Homogeneity, massive parallelism, local cellular interactions and both synchronous and asynchronous models of rule execution are some of their most prominent features, allowing scientists to model and understand a variety of phenomena in, to name but a few, the physical, chemical, biological, social and information sciences. An ubiquitous problem related with the study of complex systems by means of CA is that of parameter identification. In some cases, analytical methods are available but in many others, due to the bottom-up complexity of the underlying processes, the best route for CA identification is through design optimization by means of a metaheuristic, such as an evolutionary algorithm. In this work we report on a systematic methodology we have developed to control the spatio-temporal behavior of a CA in order to obtain a ‘designoid’ target pattern. Four independent CA-based complex systems were used to assess our hypothesis which combines clustering, fitness distance correlation and evolutionary algorithms. 1 INTRODUCTION Understanding how nature produces and relies upon natural phenomena, such as self-organization, evolution by natural selection, etc., to construct the magnificent engineering solutions routinely found in nature (e.g. eyes, lungs, brains, wings, etc.) is of enormous scientific and technical relevance. Self-organisation, both in the temporal and spatial domain, is a common 77