Doctoral Degrees (Computer Science)

Permanent URI for this collectionhttps://hdl.handle.net/10413/7113

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Automated design of genetic programming of classification algorithms.
(2018) Nyathi, Thambo.; Pillay, Nelishia.
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,ageneticalgorithm(GA)andgrammaticalevolution(GE)toautomatethe 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.
The enhanced best performance algorithm for global optimization with applications.
(2016) Chetty, Mervin.; Adewumi, Aderemi Oluyinka.
Abstract available in PDF file.
Improved roach-based algorithms for global optimization problems.
(2014) Obagbuwa, Ibidun Christiana.; Adewumi, Aderemi Oluyinka.
Optimization of systems plays an important role in various fields including mathematics, economics, engineering and life sciences. A lot of real world optimization problems exist across field of endeavours such as engineering design, space planning, networking, data analysis, logistic management, financial planning, risk management, and a host of others. These problems are constantly increasing in size and complexity, necessitating the need for improved techniques. Many conventional approaches have failed to solve complex problems effectively due to increasingly large solution space. This has led to the development of evolutionary algorithms that draw inspiration from the process of natural evolution. It is believed that nature provides inspirations that can lead to innovative models or techniques for solving complex optimization problems. Among the class of paradigm based on this inspiration is Swarm Intelligence (SI). SI is one of the recent developments of evolutionary computation. A SI paradigm is comprised of algorithms inspired by the social behaviour of animals and insects. SI-based algorithms have attracted interest, gained popularity and attention because of their flexibility and versatility. SIbased algorithms have been found to be efficient in solving real world optimization problems. Examples of SI algorithms include Ant Colony Optimization (ACO) inspired by the pheromone trail-following behaviour of ant species; Particle Swarm Optimization (PSO) inspired by flocking and swarming behaviour of insects and animals; and Bee Colony Optimization (BCO) inspired by bees’ food foraging. Recent emerging techniques in SI includes Roach-based Algorithms (RBA) motivated by cockroaches social behaviour. Two recently introduced RBA algorithms are Roach Infestation Optimization (RIO) and Cockroach Swarm Optimization (CSO) which have been applied to some optimization problems to achieve competitive results when compared to PSO. This study is motivated by the promising results of RBA, which have shown that the algorithms have potentials to be efficient tools for solving optimization problems. Extensive studies of existing RBA were carried out in this work revealing the shortcomings such as slow convergence and entrapment in local minima. The aim of this study is to overcome the identified drawbacks. We investigate RBA variants that are introduced in this work by introducing parameters such as constriction factor and sigmoid function that have proved effective for similar evolutionary algorithms in the literature. In addition components such as vigilance, cannibalism and hunger are incorporated into existing RBAs. These components are constructed by the use of some known techniques such as simple Euler, partial differential equation, crossover and mutation methods to speed up convergence and enhance the stability, exploitation and exploration of RBA. Specifically, a stochastic constriction factor was introduced to the existing CSO algorithm to improve its performance and enhance its ability to solve optimization problems involving thousands of variables. A CSO algorithm that was originally designed with three components namely chase-swarming, dispersion and ruthlessness is extended in this work with hunger component to improve its searching ability and diversity. Also, predator-prey evolution using crossover and mutation techniques were introduced into the CSO algorithm to create an adaptive search in each iteration thereby making the algorithm more efficient. In creating a discrete version of a CSO algorithm that can be used to evaluate optimization problems with any discrete range value, we introduced the sigmoid function. Furthermore, a dynamic step-size adaptation with simple Euler method was introduced to the existing RIO algorithm enhancing swarm stability and improving local and global searching abilities. The existing RIO model was also re-designed with the inclusion of vigilance and cannibalism components. The improved RBA were tested on established global optimization benchmark problems and results obtained compared with those from the literature. The improved RBA introduced in this work show better improvements over existing ones.
The investigation into an algorithm based on wavelet basis functions for the spatial and frequency decomposition of arbitrary signals.
(1994) Goldstein, Hilton.; Sartori-Angus, Alan G.
The research was directed toward the viability of an O(n) algorithm which could decompose an arbitrary signal (sound, vibration etc.) into its time-frequency space. The well known Fourier Transform uses sine and cosine functions (having infinite support on t) as orthonormal basis functions to decompose a signal i(t) in the time domain to F(w) in the frequency . domain, where the Fourier coefficients F(w) are the contributions of each frequency in the original signal. Due to the non-local support of these basis functions, a signal containing a sharp localised transient does not have localised coefficients, but rather coefficients that decay slowly. Another problem is that the coefficients F(w) do not convey any time information. The windowed Fourier Transform, or short-time Fourier Transform, does attempt to resolve the latter, but has had limited success. Wavelets are basis functions, usually mutually orthonormal, having finite support in t and are therefore spatially local. Using non-orthogonal wavelets, the Dominant Scale Transform (DST) designed by the author, decomposes a signal into its approximate time-frequency space. The associated Dominant Scale Algorithm (DSA) has O(n) complexity and is integer-based. These two characteristics make the DSA extremely efficient. The thesis also investigates the problem of converting a music signal into it's equivalent music score. The old problem of speech recognition is also examined. The results obtained from the DST are shown to be consistent with those of other authors who have utilised other methods. The resulting DST coefficients are shown to render the DST particularly useful in speech segmentation (silence regions, voiced speech regions, and frication). Moreover, the Spectrogram Dominant Scale Transform (SDST), formulated from the DST, was shown to approximate the Fourier coefficients over fixed time intervals within vowel regions of human speech.
Studies in particle swarm optimization technique for global optimization.
(2013) Martins, Arasomwan Akugbe.; Adewumi, Aderemi Oluyinka.
Abstract available in the digital copy.

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Browsing Doctoral Degrees (Computer Science) by Subject "Algorithms."