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USGS · 70024633

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

S.J. Louis·G. L. Raines
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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.

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S.J. Louis, G. L. Raines. 2003. Genetic Algorithm Calibration of Probabilistic Cellular Automata for Modeling Mining Permit Activity. https://pubs.usgs.gov/publication/70024633

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