Fuzzy Evolutionary Cellular Automata

ABSRACT: An application of Genetic Algorithms to search for optimal Cellular Automata rules to solve the density classification task is presented. A review of recent work is detailed along with a study of the statistical significance of previous results. A review of powerful Genetic Algorithm enhancements is also presented, with the aim of demonstrating marked improvement in the robustness and optimization capability of the GA. These techniques are then applied to the Evolutionary Cellular Automata model to show improvement in convergence speed and more effective search of the optimization landscape.

Master's Degree at Utah State University

Thesis Committee

Professor Nick Flann, Computer Science Professor David Peak, Physics Professor Don Cooley, Computer Science

Thesis - Final Format [PDF]

The thesis is an extension of the basic Evolutionary Cellular Automata (EvCA) research done by the EvCA group at the Santa Fe Institute. http://www.santafe.edu/projects/evca/