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 Globally Synchronized Cellular Automata
Proceedings of the 6th International Conference on Genetic Algorithms
ECAL'05 Proceedings of the 8th European conference on Advances in Artificial Life
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Various investigations have been carried out on the computational power of cellular automata (CA), with concentrated efforts in the study of one-dimensional CA. One of the approaches is the use of genetic algorithms (GA) to look for CA with a predefined computational behavior. We have previously shown a set of parameters that can be effective in helping forecast CA dynamic behavior; here, they are used as an heuristic to guide the GA search, by biasing selection, mutation and crossover, in the context of the Grouping Task (GT) for one-dimensional CA. Since GT is a new task, no a priori knowledge about its solutions is presently available; even then, the incorporation of the parameter-based heuristic entails a significant improvement over the results achieved by the plain genetic search.