Hybrid Graph Heuristics within a Hyper-heuristic Approach to Exam Timetabling Problems

This paper is concerned with the hybridization of two graph coloring heuristics (Saturation Degree and Largest Degree), and their application within a hyperheuristic for exam timetabling problems. Hyper-heuristics can be seen as algorithms which intelligently select appropriate algorithms/heuristics for solving a problem. We developed a Tabu Search based hyper-heuristic to search for heuristic lists (of graph heuristics) for solving problems and investigated the heuristic lists found by employing knowledge discovery techniques. Two hybrid approaches (involving Saturation Degree and Largest Degree) including one which employs Case Based Reasoning are presented and discussed. Both the Tabu Search based hyper-heuristic and the hybrid approaches are tested on random and real-world exam timetabling problems. Experimental results are comparable with the best state-of-the-art approaches (as measured against established benchmark problems). The results also demonstrate an increased level of generality in our approach

A New Initialisation Method for Examination Timetabling Heuristics

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record.Timetabling problems are widespread, but are particularly prevalent in the educational domain. When sufficiently large, these are often only effectively tackled by timetabling meta-heuristics. The effectiveness of these in turn are often largely dependant on their initialisation protocols. There are a number of different initialisation approaches used in the literature for starting examination timetabling heuristics. We present a new iterative initialisation algorithm here --- which attempts to generate high-quality and legal solutions, to feed into a heuristic optimiser. The proposed approach is empirically verified on the ITC 2007 and Yeditepe benchmark sets. It is compared to popular initialisation approaches commonly employed in exam timetabling heuristics: the largest degree, largest weighted degree, largest enrollment, and saturation degree graph-colouring approaches, and random schedule allocation. The effectiveness of these approaches are also compared via incorporation in an exemplar evolutionary algorithm. The results show that the proposed method is capable of producing feasible solutions for all instances, with better quality and diversity compared to the alternative methods. It also leads to improved optimiser performance.Saudi Arabia Cultural Burea

Target detection with morphological shared-weight neural network : different update approaches

Neural networks are widely used for image processing. Of these, the convolutional neural network (CNN) is one of the most popular. However, the CNN needs a large amount of training data to improve its accuracy. If training data is limited, a morphological shared-weight neural network (MSNN) can be a better choice. In this thesis, two different update approaches based on an evolutionary algorithm are proposed and compared to each other for target detection based on the MSNN. Another network training, based on back propagation, is used for comparisons in this thesis, which was proposed by Yongwan Won and applied by my colleague and fellow graduate student, Shuxian Shen and Anes Ouadou. Single-layer and multiple-layer MSNNs are both presented with different approaches. For a dataset, the author created part of a dataset for this thesis and used another dataset created by Shen to make comparisons with her network. Results of the MSNN are compared with CNN results to show the performance. Experiments show that for a single-layer MSNN, the performance of an evolutionary algorithm with partial backpropagation is the best. For a multiple layer MSNN, backpropagation performs better, although the MSNN still has a better performance than the CNN.Includes bibliographical reference

Resolução de horários de exames com pesquisa local restringida

Dissertação para obtenção do Grau de Mestre em Engenharia InformáticaA produção de horários é um problema altamente combinatório dado o conjunto de restrições que envolve e as suas interdependências, bem como um conjunto de preferências e características de qualidade que são difíceis de especificar e mais ainda de quantificar. Não sendo razoável explorar todo o espaço de pesquisa dada a sua dimensão exponencial, a utilização de pesquisa local torna-se apelativa, em parte porque a definição de vizinhanças pode ser feita de uma forma bastante intuitiva, permitindo a validação e comparação com métodos manuais de resolução do problema. Recentemente, começam a aparecer linguagens e ferramentas para pesquisa local restringida que favorecem a declaratividade da especificação de pesquisa local e que se têm revelado altamente competitivas na resolução de vários tipos de problemas combinatórios. Nesta dissertação, é testada esta abordagem na produção de horários. Mais especificamente, são analisadas o tipo de restrições, preferências e medidas de qualidade que ocorrem na especificação de horários. Subsequentemente, após a análise das vizinhanças a utilizar na resolução destes problemas, implementou-se um protótipo, bem como as meta-heurísticas que se revelaram mais interessantes. O protótipo implementado foi testado com exemplos de horários produzidos na FCT/UNL e as várias meta-heurísticas implementadas foram testadas com o benchmark da Universidade de Toronto

Evaluating Particle Swarm Intelligence Techniques for Solving University Examination Timetabling Problems

The purpose of this thesis is to investigate the suitability and effectiveness of the Particle Swarm Optimization (PSO) technique when applied to the University Examination Timetabling problem. We accomplished this by analyzing experimentally the performance profile-the quality of the solution as a function of the execution time-of the standard form of the PSO algorithm when brought to bear against the University Examination Timetabling problem. This study systematically investigated the impact of problem and algorithm factors in solving this particular timetabling problem and determined the algorithm\u27s performance profile under the specified test environment. Keys factors studied included problem size (i.e., number of enrollments), conflict matrix density, and swarm size. Testing used both real world and fabricated data sets of varying size and conflict densities. This research also provides insight into how well the PSO algorithm performs compared with other algorithms used to attack the same problem and data sets. Knowing the algorithm\u27s strengths and limitations is useful in determining its utility, ability, and limitations in attacking timetabling problems in general and the University Examination Timetabling problem in pal1icular. Finally, two additional contributions were made during the course of this research: a better way to fabricate examination timetabling data sets and the introduction of the PSO-No Conflicts optimization approach. Our new data set fabrication method produced data sets that were more representative of real world examination timetabling data sets and permitted us to construct data sets spanning a wide range of sizes and densities.· The newly derived PSO-No Conflicts algorithm permitted the PSO algorithm to perform searches while still satisfying constraints

Evolutionary multi-objective optimization in scheduling problems

Ph.DDOCTOR OF PHILOSOPH