Automated design of genetic programming of classification algorithms
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gp-bibliography.bib Revision:1.8154
- @PhdThesis{Nyathi_Thambo_2018,
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author = "Thambo Nyathi",
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title = "Automated design of genetic programming of
classification algorithms",
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school = "University of KwaZulu-Natal",
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year = "2018",
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address = "Pietermaritzburg, South Africa",
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month = "4 " # dec,
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keywords = "genetic algorithms, genetic programming, Grammatical
evolution",
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URL = "https://researchspace.ukzn.ac.za/handle/10413/17394",
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URL = "https://ukzn-dspace.ukzn.ac.za/bitstream/handle/10413/17394/Nyathi_Thambo_2018.pdf",
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URL = "https://core.ac.uk/download/304374254.pdf",
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size = "161 pages",
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abstract = "Over the past decades, there has been an increase in
the use of evolutionary algorithms (EAs) for data
mining and knowledge discovery in a wide range of
application domains. Data classification, a real-world
application problem is one of the areas EAs have been
widely applied. Data classification has been
extensively researched resulting in the development of
a number of EA based classification algorithms. Genetic
programming (GP) in particular has been shown to be one
of the most effective EAs at inducing classifiers. It
is widely accepted that the effectiveness of a
parameterised algorithm like GP depends on its
configuration. Currently, the design of GP
classification algorithms is predominantly performed
manually. Manual design follows an iterative trial and
error approach which has been shown to be a menial,
non-trivial time-consuming task that has a number of
vulnerabilities. The research presented in this thesis
is part of a large-scale initiative by the machine
learning community to automate the design of machine
learning techniques. The study investigates the
hypothesis that automating the design of GP
classification algorithms for data classification can
still lead to the induction of effective classifiers.
This research proposes using two evolutionary
algorithms,namely, a genetic algorithm (GA) and
grammatical evolution (GE) to automate the design of GP
classification algorithms. The proof-by-demonstration
research methodology is used in the study to achieve
the set out objectives. To that end two systems namely,
a genetic algorithm system and a grammatical evolution
system were implemented for automating the design of GP
classification algorithms. The classification
performance of the automated designed GP classifiers,
i.e., GA designed GP classifiers and GE designed GP
classifiers were compared to manually designed GP
classifiers on real-world binary class and multiclass
classification problems. The evaluation was performed
on multiple domain problems obtained from the UCI
machine learning repository and on two specific
domains, cybersecurity and financial forecasting. The
automated designed classifiers were found to outperform
the manually designed GP classifiers on all the
problems considered in this study. GP classifiers
evolved by GE were found to be suitable for classifying
binary classification problems while those evolved by a
GA were found to be suitable for multiclass
classification problems. Furthermore, the automated
design time was found to be less than manual design
time. Fitness landscape analysis of the design spaces
searched by a GA and GE were carried out on all the
class of problems considered in this study. Grammatical
evolution found the search to be smoother on binary
classification problems while the GA found multiclass
problems to be less rugged than binary class
problems.",
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notes = "Supervisor: Nelishia Pillay",
- }
Genetic Programming entries for
Thambo Nyathi
Citations