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An Evolutionary Methodology for the Automated Design of Cellular Automaton-based Complex Systems
Germán Terrazas, Peter Siepmann, Graham Kendall and Natali O. Krasnogor

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.

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