Predicting mining activity with parallel genetic algorithms
By: S. Talaie, R. Leigh, S.J. Louis, and G. L. Raines
Edited by: H.G. Beyer, U.M. O'Reilly, Arnold D. Banzhaf, W. Blum, C. Bonabeau, E.W. Cantu-Paz, E.Dasgupta, and and others
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Abstract
We explore several different techniques in our quest to improve the overall model performance of a genetic algorithm calibrated probabilistic cellular automata. We use the Kappa statistic to measure correlation between ground truth data and data predicted by the model. Within the genetic algorithm, we introduce a new evaluation function sensitive to spatial correctness and we explore the idea of evolving different rule parameters for different subregions of the land. We reduce the time required to run a simulation from 6 hours to 10 minutes by parallelizing the code and employing a 10-node cluster. Our empirical results suggest that using the spatially sensitive evaluation function does indeed improve the performance of the model and our preliminary results also show that evolving different rule parameters for different regions tends to improve overall model performance. Copyright 2005 ACM.
| Publication type | Conference Paper |
|---|---|
| Publication Subtype | Conference Paper |
| Title | Predicting mining activity with parallel genetic algorithms |
| ISBN | 1595930108 |
| DOI | 10.1145/1068009.1068363 |
| Year Published | 2005 |
| Language | English |
| Larger Work Title | GECCO 2005 - Genetic and Evolutionary Computation Conference |
| First page | 2149 |
| Last page | 2155 |
| Conference Title | GECCO 2005 - Genetic and Evolutionary Computation Conference |
| Conference Location | Washington, D.C. |
| Conference Date | 25 June 2005 through 29 June 2005 |
| Google Analytic Metrics | Metrics page |