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Genetic Algorithm Calibration of Probabilistic Cellular Automata for Modeling Mining Permit Activity

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Abstract

We use a genetic algorithm to calibrate a spatially and temporally resolved cellular automata to model mining activity on public land in Idaho and western Montana. The genetic algorithm searches through a space of transition rule parameters of a two dimensional cellular automata model to find rule parameters that fit observed mining activity data. Previous work by one of the authors in calibrating the cellular automaton took weeks - the genetic algorithm takes a day and produces rules leading to about the same (or better) fit to observed data. These preliminary results indicate that genetic algorithms are a viable tool in calibrating cellular automata for this application. Experience gained during the calibration of this cellular automata suggests that mineral resource information is a critical factor in the quality of the results. With automated calibration, further refinements of how the mineral-resource information is provided to the cellular automaton will probably improve our model.

Additional publication details

Publication type Conference Paper
Publication Subtype Conference Paper
Title Genetic Algorithm Calibration of Probabilistic Cellular Automata for Modeling Mining Permit Activity
Year Published 2003
Language English
Larger Work Title Proceedings of the International Conference on Tools with Artificial Intelligence
First page 515
Last page 519
Conference Title Proceedings: 15th IEEE International Conference on Tools with artificial Intelligence
Conference Location Sacramento, CA
Conference Date 3 November 2003 through 5 November 2003