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
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
Open problems in artificial life
Artificial Life - Special issue on the Artificial Life VII: looking backward, looking forward
Parallel Computing - Special issue on cellular automata: from modeling to applications
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Searching for One-Dimensional Cellular Automata in the Absence of a priori Information
ECAL '01 Proceedings of the 6th European Conference on Advances in Artificial Life
Two-dimensional cellular automata of radius one for density classification task ρ = ½
Pattern Recognition Letters
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Resource sharing and coevolution in evolving cellular automata
IEEE Transactions on Evolutionary Computation
Electronic Notes in Theoretical Computer Science (ENTCS)
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The investigations carried out about the relationships between the generic dynamic behavior of cellular automata (CA) and their computational abilities have established a very active research area. Evolutionary methods have been used to look for CA with predefined computational abilities; one in particular that has been widely studied is the ability to solve the density classification task (DCT). The majority of these studies are focused on the one-dimensional CA. It has recently been shown that the use of a heuristic guided by parameters that estimate the dynamic behavior of 1D CA can improve the evolutionary search for DCT. The present work shows the application of three parameters previously published in the one-dimensional context generalized to the two-dimensional space: sensitivity, neighborhood dominance and activity propagation were used to evolve CA able to perform the two-dimensional version of the density classification task. The results obtained show that the parameters can effectively help a genetic algorithm in searching for 2D CA. A new rule was found which performed better than others previously published for the 2D DCT.