Computation at the edge of chaos: phase transitions and emergent computation
CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
An introduction to genetic algorithms
An introduction to genetic algorithms
Computing in nonlinear media and automata collectives
Computing in nonlinear media and automata collectives
Mean Field Theory of the Edge of Chaos
Proceedings of the Third European Conference on Advances in Artificial Life
Complexity
The use of cellular automata in the learning of emergence
Computers & Education
A Neuro-Genetic Framework for Pattern Recognition in Complex Systems
Fundamenta Informaticae - Membrane Computing
A Neuro-Genetic Framework for Pattern Recognition in Complex Systems
Fundamenta Informaticae - Membrane Computing
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An important problem in the theory of cellular automata (CA) is the research of complex rules, the parameterization of the rule space, and the description of the CA dynamic behavior. In this article, the authors used evolutionary techniques in the search of CA with specific characteristics, in order to understand how their data are related to the previous findings in this sector. Using as fitness function, the variance of the input-entropy, a genetic algorithm was built to search complex rules for multistate CA. The algorithm used is efficient and many complex rules have been found. Many of these rules present interesting characteristics. Gliders with large period and large spatial dimensions may be observed. The experiments performed show that complex rules have several λ values, which do not reside in a sharply defined region; on the contrary, these values are extended on very large regions of the λ parameter. The results provide also a view of the CA rule space.