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Predicting mining activity with parallel genetic algorithms

By:
, , , and
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
DOI: 10.1145/1068009.1068363

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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.

Additional Publication Details

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
Number of Pages:
7
Conference Title:
GECCO 2005 - Genetic and Evolutionary Computation Conference
Conference Location:
Washington, D.C.
Conference Date:
25 June 2005 through 29 June 2005