Evolving cellular automata to perform computations: mechanisms and impediments
Proceedings of the NATO advanced research workshop and EGS topical workshop on Chaotic advection, tracer dynamics and turbulent dispersion
A brief history of cellular automata
ACM Computing Surveys (CSUR)
Parallel Computing - Special issue on cellular automata: from modeling to applications
A new kind of science
An Introduction to Genetic Algorithms
An Introduction to Genetic Algorithms
Electronic Notes in Theoretical Computer Science (ENTCS)
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Performing computations with cellular automata, individually or arranged in space or time, opens up new conceptual issues in emergent, artificial life type forms of computation, and opens up the possibility of novel technological advances. Here, a methodology for combining sequences of elementary cellular automata is presented, in order to perform a given computation. The problem at study is the well-known density classification task that consists of determining the most frequent bit in a binary string. The methodology relies on an evolutionary algorithm, together with analyses driven by background knowledge on dynamical behaviour of the rules and their parametric estimates, as well as those associated with the formal behaviour characterisation of the rules involved. The resulting methodology builds upon a previous approach available in the literature, and shows its efficacy by leading to 2 rule combinations already known, and to additional 26, apparently unknown so far.