thumbnail

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

By:
,

Links

  • The Publications Warehouse does not have links to digital versions of this publication at this time

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
First page:
515
Last page:
519
Number of Pages:
5
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