Genetic Programming for Symbolic Regression with Portable Near-Infrared Spectroscopy for the Prevention of Harm from Adulterated Illicit Drugs at Festivals in New Zealand
Created by W.Langdon from
gp-bibliography.bib Revision:1.8576
- @MastersThesis{Dockter:MSc,
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author = "Steven E. Dockter",
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title = "Genetic Programming for Symbolic Regression with
Portable Near-Infrared Spectroscopy for the Prevention
of Harm from Adulterated Illicit Drugs at Festivals in
New Zealand",
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school = "Victoria University of Wellington",
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year = "2025",
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address = "New Zealand",
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keywords = "genetic algorithms, genetic programming, Artificial
Intelligence",
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URL = "
https://openaccess.wgtn.ac.nz/articles/thesis/Genetic_Programming_for_Symbolic_Regression_with_Portable_Near-Infrared_Spectroscopy_for_the_Prevention_of_Harm_from_Adulterated_Illicit_Drugs_at_Festivals_in_New_Zealand/28941851?file=54253688",
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URL = "
https://openaccess.wgtn.ac.nz/ndownloader/files/54253688",
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size = "114 pages",
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abstract = "Illicit drugs are often mixed with cutting agents and
adulterants, which can pose significant risks, either
independently or when consumed with the drug itself.
Drug checking services play a vital role in reducing
these risks by providing information about substance
composition and drug harm prevention. A widely adopted
model for delivering drug checking services is at
events and festivals. To effectively reduce
drug-related harm in such settings, it is crucial to
have rapid, accurate, non-destructive, safe, portable,
and user-friendly substance identification methods.
Near-infrared spectroscopy (NIRS) is a well-established
technique for identifying and quantifying a wide range
of substances, including illicit drug mixtures,
particularly with high-end bench-top instruments. While
using portable NIRS devices for mixture analysis is
advantageous, challenges such as low sensitivity,
limited wavelength range, and low resolution persist.
To address these challenges, various artificial
intelligence techniques have been applied to analyze
portable NIRS data for mixtures. However, evolutionary
computation methods, which are known for their
robustness in solving complex optimization problems,
have not been widely explored in NIRS-based mixture
analysis. This thesis proposes a genetic programming
for symbolic regression (GPSR) approach to develop
explainable models for analyzing illicit drug mixtures
using portable NIRS. Experimental results demonstrate
that the proposed approach can accurately model the
relationship between a mixture and its components based
on NIRS data. Furthermore, the method shows promise in
accurately identifying components within a drug sample,
with performance comparable to traditional linear
models. The potential for implementing this method into
an integrated solution for drug checking services at
point-of-care scenarios, such as festivals, is also
explored.",
- }
Genetic Programming entries for
Steven E Dockter
Citations