A Study of the Practical and Tutorial Scheduling Problem

Abstract: The practical and tutorial allocation problem is a problem encountered at tertiary institutions and essentially involves the allocation of students to practical or tutorial groups for the different courses the student is enrolled in. Practical and tutorial scheduling for first year courses is becoming more and more challenging as the number of permissible course combinations and student numbers increase at tertiary institutions, and while this has previously been done manually and independently for each course, this is no longer feasible. The paper firstly presents a formal definition of the practical and tutorial scheduling problem. Low-level construction heuristics for this domain are defined and a heuristic approach for solving this problem is proposed. A tool namely, PRATS, incorporating this approach is described. The performance of PRATS on six sets of real-world data is discussed. The paper also reports on a hyper-heuristic implemented to automatically generate low-level construction heuristics and compares the performance of the generated heuristics to the human intuitive heuristics used

A computer mediated system for distance education.

Thesis (M.Sc)-University of Natal, Pietermaritzburg, 1996.A problem currently facing South Africa is the large number of poorly educated or uneducated people in many parts of the country. Distance education has proven to be an apt solution to this problem However, one of the numerous constraints associated with studying at a distance is insufficient communication between students and lecturers and the lack of peer interaction. The integration of Computer Mediated Communications (CMC) in the delivery of distance education courses world-wide has proved to be a means of alleviating this communication problem. The study presented in this thesis examines the technical feasibility of implementing CMC in the delivery of South African distance education courses as a solution to the communication problems experienced by distance learners in this country. For this purpose a system was developed and implemented at a South African distance education institution namely, Natal College of Education in Pietermaritzburg. Based on this implementation a technical evaluation of the feasibility of CMC in the instruction of distance education courses within a South African infrastructure was examined. As a result of this study we have been able to: • Determine the technical problems associated with the implementation of a CMC system in a South African distance education environment. • Identify possible solutions to these technical problems • Define a set of criteria, which if met by a CMC system would ensure the technical feasibility of the system as a solution to the communication problems experienced by South African distance learners. • Determine the effects of students' attitudes towards computers on their use of the CMC system. • Determine the effect of CMC on students' attitudes towards computers. • Identify any additional factors, besides technical issues, which need to be taken into account when implementing a CMC system

The impact of genetic programming in education

Since its inception genetic programming, and later variations such as grammar-based genetic programming and grammatical evolution, have contributed to various domains such as classification, image processing, search-based software engineering, amongst others. This paper examines the role that genetic programming has played in education. The paper firstly provides an overview of the impact that genetic programming has had in teaching and learning. The use of genetic programming in intelligent tutoring systems, predicting student performance and designing learning environments is examined. A critical analysis of genetic programming in education is provided. The paper then examines future directions of research and challenges in the application of genetic programming in education.http://link.springer.com/journal/107102020-07-26hj2020Computer Scienc

The General Combinatorial Optimization Problem: Towards Automated Algorithm Design

This paper defines a new combinatorial optimisation problem, namely General Combinatorial Optimisation Problem (GCOP), whose decision variables are a set of parametric algorithmic components, i.e. algorithm design decisions. The solutions of GCOP, i.e. compositions of algorithmic components, thus represent different generic search algorithms. The objective of GCOP is to find the optimal algorithmic compositions for solving the given optimisation problems. Solving the GCOP is thus equivalent to automatically designing the best algorithms for optimisation problems. Despite recent advances, the evolutionary computation and optimisation research communities are yet to embrace formal standards that underpin automated algorithm design. In this position paper, we establish GCOP as a new standard to define different search algorithms within one unified model. We demonstrate the new GCOP model to standardise various search algorithms as well as selection hyper-heuristics. A taxonomy is defined to distinguish several widely used terminologies in automated algorithm design, namely automated algorithm composition, configuration and selection. We would like to encourage a new line of exciting research directions addressing several challenging research issues including algorithm generality, algorithm reusability, and automated algorithm design

Transfer learning in evolutionary spaces

Paper presented at GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion, July 2022.No abstract available.This work is based on the research supported in part by the National Research Foundation of South Africa.https://dl.acm.org/doi/10.1145/3520304.3533632hj202

Neural network crossover in genetic algorithms using genetic programming

The use of genetic algorithms (GAs) to evolve neural network (NN) weights has risen in popularity in recent years, particularly when used together with gradient descent as a mutation operator. However, crossover operators are often omitted from such GAs as they are seen as being highly destructive and detrimental to the performance of the GA. Designing crossover operators that can effectively be applied to NNs has been an active area of research with success limited to specific problem domains. The focus of this study is to use genetic programming (GP) to automatically evolve crossover operators that can be applied to NN weights and used in GAs. A novel GP is proposed and used to evolve both reusable and disposable crossover operators to compare their efficiency. Experiments are conducted to compare the performance of GAs using no crossover operator or a commonly used human designed crossover operator to GAs using GP evolved crossover operators. Results from experiments conducted show that using GP to evolve disposable crossover operators leads to highly effectively crossover operators that significantly improve the results obtained from the GA.Open access funding provided by University of Pretoria. This work was funded as part of the Multichoice Research Chair in Machine Learning at the University of Pretoria, South Africa. This work is based on the research supported in part by the National Research Foundation of South Africa.http://link.springer.com/journal/10710hj2024Computer ScienceSDG-09: Industry, innovation and infrastructur

A genetic programming approach to the automated design of CNN models for image classification and video shorts creation

Neural architecture search (NAS) is a rapidly growing field which focuses on the automated design of neural network architectures. Genetic algorithms (GAs) have been predominantly used for evolving neural network architectures. Genetic programming (GP), a variation of GAs that work in the program space rather than a solution space, has not been as well researched for NAS. This paper aims to contribute to the research into GP for NAS. Previous research in this field can be divided into two categories. In the first each program represents neural networks directly or components and parameters of neural networks. In the second category each program is a set of instructions, which when executed, produces a neural network. This study focuses on this second category which has not been well researched. Previous work has used grammatical evolution for generating these programs. This study examines canonical GP for neural network design (GPNND) for this purpose. It also evaluates a variation of GP, iterative structure-based GP (ISBGP) for evolving these programs. The study compares the performance of GAs, GPNND and ISBGP for image classification and video shorts creation. Both GPNND and ISBGP were found to outperform GAs, with ISBGP producing better results than GPNND for both applications. Both GPNND and ISBGP produced better results than previous studies employing grammatical evolution on the CIFAR-10 dataset.Open access funding provided by University of Pretoria. This work was funded as part of the Multichoice Research Chair in Machine Learning at the University of Pretoria, South Africa. This work is based on the research supported in part by the National Research Foundation of South Africa.http://link.springer.com/journal/10710hj2024Computer ScienceSDG-09: Industry, innovation and infrastructur

Genetic programming-based regression for temporal data

Various machine learning techniques exist to perform regression on temporal data with concept drift occurring. However, there are numerous nonstationary environments where these techniques may fail to either track or detect the changes. This study develops a genetic programming-based predictive model for temporal data with a numerical target that tracks changes in a dataset due to concept drift. When an environmental change is evident, the proposed algorithm reacts to the change by clustering the data and then inducing nonlinear models that describe generated clusters. Nonlinear models become terminal nodes of genetic programming model trees. Experiments were carried out using seven nonstationary datasets and the obtained results suggest that the proposed model yields high adaptation rates and accuracy to several types of concept drifts. Future work will consider strengthening the adaptation to concept drift and the fast implementation of genetic programming on GPUs to provide fast learning for high-speed temporal data.http://link.springer.com/journal/107102022-05-09hj2021Computer Scienc

Assessing hyper-heuristic performance

Limited attention has been paid to assessing the generality performance of hyper-heuristics. The performance of hyper-heuristics has been predominately assessed in terms of optimality which is not ideal as the aim of hyper-heuristics is not to be competitive with state of the art approaches but rather to raise the level of generality, i.e. the ability of a technique to produce good results for different problem instances or problems rather than the best results for some instances and poor results for others. Furthermore from existing literature in this area it is evident that different hyper-heuristics aim to achieve different levels of generality and need to be assessed as such. To cater for this the paper firstly presents a new taxonomy of four different levels of generality that can be attained by a hyper-heuristic based on a survey of the literature. The paper then proposes a performance measure to assess the performance of different types of hyper-heuristics at the four levels of generality in terms of generality rather than optimality. Three case studies from the literature are used to demonstrate the application of the generality performance measure. The paper concludes by examining how the generality measure can be combined with measures of other performance criteria, such as optimality, to assess hyper-heuristic performance on more than one criterion

Automated design of the deep neural network pipeline

Deep neural networks have proven to be effective in various domains, especially in natural language processing and image processing. However, one of the challenges associated with using deep neural networks includes the long design time and expertise needed to apply these neural networks to a particular domain. The research presented in this paper investigates the automation of the design of the deep neural network pipeline to overcome this challenge. The deep learning pipeline includes identifying the preprocessing needed, the feature engineering technique, the neural network to use and the parameters for the neural network. A selection pertubative hyper-heuristic (SPHH) is used to automate the design pipeline. The study also examines the reusability of the generated pipeline. The effectiveness of transfer learning on the generated designs is also investigated. The proposed approach is evaluated for text processing—namely, sentiment analysis and spam detection— and image processing—namely, maize disease detection and oral lesion detection. The study revealed that the automated design of the deep neural network pipeline produces just as good, and in some cases better, performance compared to the manual design, with the automated design requiring less design time than the manual design. In the majority of instances, the design was not reusable; however, transfer learning achieved positive transfer of designs, with the performance being just as good or better than when transfer learning was not used.The National Research Foundation of South Africa.https://www.mdpi.com/journal/applsciam2023Computer Scienc