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.
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