Genetic Algorithm Calibration of Probabilistic Cellular Automata for Modeling Mining Permit Activity
By: S.J. Louis and 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.
| 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 |
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