Calibration of Genetic Algorithm Parameters for Mining-Related Optimization Problems
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Genetic algorithms (GA) are widely used to solve engineering optimization problems. The quality and performance of the solution generated strongly depend on the selection of the GA parameter values (crossover and mutation rates and population size). We propose an approach based on full factorial and response surface methodology experimental designs to calibrate GA parameters such that the objective function is maximized/minimized and the relative importance of the parameters is quantified. The approach was tested by applying it to stope optimization of underground mines, where profit can vary ± 7% based solely on GA parameters. Results showed that: (1) a larger population size did not always increase solution time; (2) solution time was positively related to crossover and mutation rates; and (3) simultaneous analysis of solution time and profit illustrated the trade-off between acceptable computing time and profit desirability through GA parameter selection. This approach can be used to calibrate parameters of other metaheuristics.
KeywordsUnderground mine planning Genetic algorithms (GA) GA parameters Stope layout optimization Response surface methodology
This research was conducted with financial support from the Natural Sciences and Engineering Research Council of Canada (NSERC Fund # 242984), and we thank NSERC for this support.
- Box, G. E., & Wilson, K. B. (1951). On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society (Series B), 13, 1–45.Google Scholar
- Deutsch, C. V., & Journel, A. G. (1998). Geostatistical software library and user’s guide. New York: Oxford University Press.Google Scholar
- Johnson, R. A., & Wichern, D. W. (2007). Applied multivariate statistical analysis (6th ed.). New Jersey: Prentice-Hall.Google Scholar
- Manchuk, J., & Deutsch, C. V. (2008). Optimizing stope designs and sequences in underground mines. SME Transactions, 324, 67–75.Google Scholar
- Melvin, T. (2000). Response surface optimization using JMP Software. Baltimore: Qualistics.Google Scholar
- Mitchell, M. (1999). An introduction to genetic algorithms. Cambridge: Massachusetts Institute of Technology.Google Scholar
- Montgomery, D. C. (1997). Design and analysis of experiments. New York: Wiley.Google Scholar
- Nannen, V., & Eiben, A. E. (2007). Relevance estimation and value calibration of evolutionary algorithm parameters. Paper presented at the 20th international joint conference on artificial intelligence, Hyderabad, India,Google Scholar
- Osman, I. H., & Laporte, G. (1996). Metaheuristics: A bibliography. New York: Springer.Google Scholar
- Rayward-Smith, V. J. (1996). Modern heuristic techniques. In V. J. Rayward-Smith, I. H. Osman, C. R. Reeves, & G. D. Smith (Eds.), Modern heuristic search methods (pp. 1–25). New York: Wiley.Google Scholar
- Sauvageau, M., & Kumral, M. (2016). Genetic algorithms for the optimisation of the Schwartz-Smith two-factor model: A case study on a copper deposit. International Journal of Mining, Reclamation and Environment, 32, 1–19.Google Scholar
- Snyman, J. (2005). Practical mathematical optimization: An introduction to basic optimization theory and classical and new gradient-based algorithms (Vol. 97). New York: Springer.Google Scholar
- Telford, J. K. (2007). A brief introduction to design of experiments. Johns Hopkins APL Technical Digest, 27(3), 224–232.Google Scholar
- Verhoeff, R. L. A. (2017). Using genetic algorithms for underground stope design optimization in mining. A stochastic analysis. M.Sc. thesis, Delft University of Technology.Google Scholar
- Villalba, M. E., & Kumral, M. (2018a). Underground mine planning: Stope layout optimization under uncertainty using genetic algorithms. International Journal of Mining, Reclamation and Environment (in press). https://doi.org/10.1080/17480930.2018.1486692.
- Villalba, M. E., & Kumral, M. (2018b). A value adding approach to hard-rock underground mining operations: Balancing orebody orientation and mining direction (under submission).Google Scholar