Lecture Notes in Economics and Mathematical Systems

Hyper-Heuristics: Theory and Applications

Although there has been a fair amount of research in the area of school timetabling, this domain has not developed as well as other fields of educational timetabling such as university course and examination timetabling This can possibly be attributed to the fact that the studies in this domain have generally been conducted in isolation of each other and have addressed different school timetabling problems Furthermore, there have been no comparative studies on the success of different methodologies on a variety of school timetabling problems As a way forward this paper provides an overview of the research conducted in this domain, details of problem sets which are publicly available and proposes areas for further research in school timetabling

A survey of school timetabling research

The use of mobile robots is growing every day. Path planning algorithms are needed to allow the coordination of several robots, and make them travel with the least cost and without collisions. With this emerged the interest in studying some path planning algorithms, in order to better understand the operation of each one when applied in this type of robots. The objective of this paper is to present a state of the art survey of some algorithms of path planning for mobile robots. A brief introduction on mobile robots and trajectory planning algorithms is made. After, the basis of each algorithm is explained, their relative advantages and disadvantages are presented and are mentioned areas of application for each of them. This study was developed in order to implement some of these algorithms in the near future, with the objective to find out their relative advantages and disadvantages, and in which situations their implementation is more adequate.

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A Survey on Path Planning Algorithms for Mobile Robots

An adoption model for virtual reality games: The roles of presence and enjoyment

There has been a large amount of research into the automatic generation of school timetables Methodologies such as constraint programming, simulated annealing, Tabu search and genetic algorithms have been applied to the school timetabling problem However, a majority of these studies focus on solving the problem for a particular school and there is very little research into the comparison of the performance of different techniques in solving the school timetabling problemThe study presented in this paper evaluates genetic algorithms (GAs) for the purpose of inducing school timetables For each problem, the GA implemented iteratively refines an initial population of school timetables using mutation to find a good quality feasible timetable The performance of the GA on a set of five benchmark problems has been compared to the performance of neural networks, simulated annealing, Tabu search, and greedy search on the same set of problems The results obtained by the GA were found to be comparable to and an improvement on those produced by the other methods

An application of genetic algorithms to the school timetabling problem

In this paper we compare the performance of genetic algorithms and genetic programming in solving a set of hard school timetabling problems. Genetic algorithms search a solution space whereas genetic programming explores a program space. While previous work has examined the use of genetic algorithms in solving the school timetabling problem, there has not been any research on the use of genetic programming for this domain. The GA explores a space of timetables to find an optimal timetable. GP on the other hand searches for an optimal program which when executed will produce a solution. Each program is comprised of operators for timetable construction. The GA and GP were tested on the Abramson set of school timetabling problems. Genetic programming proved to be more effective than genetic algorithms in solving this set of problems. Furthermore, the results produced by both the GA and GP were found to be comparative to methods applied to the same set of problems.

A comparison of genetic algorithms and genetic programming in solving the school timetabling problem

The study presented in this paper applies a genetic algorithm to solve the school timetabling problem for a South African primary school and high school. The overall algorithm makes use of domain knowledge in the form of low-level heuristics to guide the search. The genetic algorithm employed to solve the problems uses tournament selection and mutation operators for the purposes of parent selection and recreation respectively. The algorithm has produced a better quality timetable for the high school problem than that generated by a previous genetic algorithm used to solve this problem. Furthermore, the genetic algorithm has induced a timetable for the primary school that is of better quality than the timetable produced by the system currently used by the school.

Using genetic algorithms to solve the South African school timetabling problem

Virtual Reality (VR) is increasingly being acknowledged as a useful platform for education. In South Africa, however, VR is mainly recognized as an entertainment platform. Hence, the potential benefits of VR and its perceived ease of use within the South African higher education setting have not been widely investigated. Therefore, using the Technology Adoption Model (TAM), this paper investigates the perceived usefulness (PU) and ease of use (PEOU) of VR by lecturers. This paper also identifies the perceived challenges to the adoption of VR as a teaching and learning platform from a higher education perspective, and suggests how those challenges may be overcome.

Lecturers’ Perceptions of Virtual Reality as a Teaching and Learning Platform

The high school timetabling problem differs drastically from one school to another and from country to country. The South African high school problem has not been researched. This paper presents a genetic algorithm (GA) to solve this problem for a particular high school. A two-phase approach is taken. The first phase uses a GA to evolve a timetable that meets the hard constraints of the problem. During the second phase a GA improves the quality of the solutions found during the first phase by reducing the soft constraint cost of the timetable. Domain knowledge, in the form of low-level construction heuristics, is used to guide the search during the first phase. The study experiments with the effect of using different low-level construction heuristics for this purpose. Each GA iteratively refines an initial population from one generation to the next by the processes of evaluation, selection and regeneration. The paper also reports on the performance of different mutation operators tested for regeneration.

An informed genetic algorithm for the high school timetabling problem

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