Enhanced Genetic Programming Models with Multiple Equations for Accurate Semi-Autogenous Grinding Mill Throughput Prediction
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @InProceedings{ghasemi:2024:CEC,
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author = "Zahra Ghasemi and Mehdi Neshat and Chris Aldrich and
John Karageorgos and Max Zanin and Frank Neumann and
Lei Chen",
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title = "Enhanced Genetic Programming Models with Multiple
Equations for Accurate Semi-Autogenous Grinding Mill
Throughput Prediction",
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booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)",
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year = "2024",
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editor = "Bing Xue",
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address = "Yokohama, Japan",
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month = "30 " # jun # " - 5 " # jul,
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publisher = "IEEE",
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keywords = "genetic algorithms, genetic programming, Measurement,
Accuracy, Training data, Euclidean distance, Predictive
models, Throughput, Prediction, Clustering, Grinding
mill",
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isbn13 = "979-8-3503-0837-2",
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DOI = "doi:10.1109/CEC60901.2024.10611827",
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abstract = "Semi-autogenous grinding (SAG) mills play a pivotal
role in the grinding circuit of mineral processing
plants. Accurate prediction of SAG mill throughput as a
crucial performance metric is of utmost importance. The
potential of applying genetic programming (GP) for this
purpose has yet to be thoroughly investigated. This
study introduces an enhanced GP approach entitled
multi-equation GP (MEGP) for more accurate prediction
of SAG mill throughput. In the new proposed method
multiple equations, each accurately predicting mill
throughput for specific clusters of training data are
extracted. These equations are then employed to predict
mill throughput for test data using various approaches.
To assess the effect of distance measures, four
different distance measures are employed in MEGP
method. Comparative analysis reveals that the best MEGP
approach achieves an average improvement of
10.74percent in prediction accuracy compared with
standard GP. In this approach all extracted equations
are used and both the number of data points in each
data cluster and the distance to clusters are
incorporated for calculating the final prediction.
Further investigation of distance measures indicates
that among four different metrics employed including
Euclidean, Manhattan, Chebyshev, and Cosine distance,
the Euclidean distance measure yields the most accurate
results for the majority of data splits.",
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notes = "also known as \cite{10611827}
WCCI 2024",
- }
Genetic Programming entries for
Zahra Ghasemi
Mehdi Neshat
Chris Aldrich
John Karageorgos
Max Zanin
Frank Neumann
Lei Chen
Citations