An evolutionary non-Linear great deluge approach for solving course timetabling problems

The aim of this paper is to extend our non-linear great deluge algorithm into an evolutionary approach by incorporating a population and a mutation operator to solve the university course timetabling problems. This approach might be seen as a variation of memetic algorithms. The popularity of evolutionary computation approaches has increased and become an important technique in solving complex combinatorial optimisation problems. The proposed approach is an extension of a non-linear great deluge algorithm in which evolutionary operators are incorporated. First, we generate a population of feasible solutions using a tailored process that incorporates heuristics for graph colouring and assignment problems. The initialisation process is capable of producing feasible solutions even for large and most constrained problem instances. Then, the population of feasible timetables is subject to a steady-state evolutionary process that combines mutation and stochastic local search. We conducted experiments to evaluate the performance of the proposed algorithm and in particular, the contribution of the evolutionary operators. The results showed the effectiveness of the hybridisation between non-linear great deluge and evolutionary operators in solving university course timetabling problems

An evolutionary non-Linear great deluge approach for solving course timetabling problems

The aim of this paper is to extend our non-linear great deluge algorithm into an evolutionary approach by incorporating a population and a mutation operator to solve the university course timetabling problems. This approach might be seen as a variation of memetic algorithms. The popularity of evolutionary computation approaches has increased and become an important technique in solving complex combinatorial optimisation problems. The proposed approach is an extension of a non-linear great deluge algorithm in which evolutionary operators are incorporated. First, we generate a population of feasible solutions using a tailored process that incorporates heuristics for graph colouring and assignment problems. The initialisation process is capable of producing feasible solutions even for large and most constrained problem instances. Then, the population of feasible timetables is subject to a steady-state evolutionary process that combines mutation and stochastic local search. We conducted experiments to evaluate the performance of the proposed algorithm and in particular, the contribution of the evolutionary operators. The results showed the effectiveness of the hybridisation between non-linear great deluge and evolutionary operators in solving university course timetabling problems

A review on spatial technologies for enhancing malaria control: concepts, tools, and challenges

This paper presents a review of numerous studies conducted on spatial technologies, tools, and applications for controlling malaria epidemiology. This paper mainly focuses on using statistical or machine learning-based models and geographic information science (GIS) and remote sensing (RS) technology for monitoring malaria disease outbreaks. The literature review includes all articles obtained from journals and conference proceedings published from 2000 through 2020 in Scopus indexed databases (e.g., Elsevier, Springer, IEEE eXplore, ACM, Wiley, and PubMed). We completed this systematic literature review using “Enhancing Malaria Control,” “GIS and Malaria Control” and “Spatial Technologies for Monitoring Malaria Disease Outbreaks” search terms. We found a total of 188 articles published in peer-reviewed journals listed in the Scopus indexed databases. After a detailed review, 152 articles were excluded because they did not meet our inclusion criteria; 36 articles were selected for the final evaluation. Several concepts and tools related to GIS applications in monitoring the malaria outbreak's spread is discussed. The discussion is categorized into four categories: a) Application of Spatial Technologies, b) Applications of Machine Learning Algorithms, c) Applying Multiple Sources of Data, and d) Applications of Smartphone Technologies. A spatial technologies framework for enhancing malaria monitoring is also proposed where it identifies the role of spatial technologies and applications in monitoring malaria disease outbreaks. The paper is concluded by providing some of the main challenges related to the issues in controlling the spread of malaria disease outbreak

Designing a multi-agent approach system for distributed course timetabling

This paper proposes tackling the difficult course timetabling problem using a multi-agent approach. The proposed design seeks to deal with the problem using a distributed solution environment in which a mediator agent coordinates various timetabling agents that cooperate to improve a common global solution. Initial timetables provided to the multi-agent system are generated using several hybrid heuristics that combine graph colouring heuristics and local search in different ways. The hybrid heuristics are capable of generating feasible timetables for all instances of the two sets of benchmark problems used here. We discuss how these initialisation hybrid heuristics can be incorporated into the proposed multi-agent approach in order to conduct distributed timetabling. This preliminary work serves as a solid basis towards the design of an effective multi-agent distributed timetabling system

An Investigation of Generality in two-layer Multi-agent Framework towards different domains

This paper proposes a two-layer multi-agent communication in two different environments. The communications in both layers of the framework are studied in order to determine the relevancy of agents to manage themselves towards different constraints across several domains. In this context, the generality of the multi-agent framework is measure by how well the agents improve the quality of solution compared with existing meta-heuristics. The two domains considered are university course timetabling and examination timetabling problems in Universiti Malaysia Sabah. The results are then compared with meta-heuristics introduced in previous studies using the same domains

Sequential constructive algorithm incorporate with fuzzy logic for solving real world course timetabling problem

Sequential constructive algorithm is one of the popular methods for solving timetabling problems. The concept of the algorithm is to assign event based on their difficulty value by using different sequential heuristic. The most common sequential heuristics are largest enrolment, largest degree and saturation degree. Each sequential heuristic has its own criteria to obtain events’ difficulty value. Instead of single sequential heuristic, this paper presents to use fuzzy logic to consider multiple sequential heuristics in order to obtain the difficulty value of the events. The proposed method will be used to generate feasible solution as well as improve the quality of the solution. Another objective of this paper is to tackle a real world course timetabling problem from Universiti Malaysia Sabah Labuan International Campus (UMSLIC). Currently, UMSLIC generates course timetable manually which is very time consuming and ineffective.The experimental results show that the proposed method is able to produce better quality of solution less than one minute. In terms of quality of timetable and efficiency, the proposed method is outperforming UMSLIC’s manual method

An evolutionary-based term reduction approach to bilingual clustering of Malay-English corpora

The document clustering process groups the unstructured text documents into a predefined set of clusters in order to provide more information to the users. There are many studies conducted in clustering monolingual documents. With the enrichment of current technologies, the study of bilingual clustering would not be a problem. However clustering bilingual document is still facing the same problem faced by a monolingual document clustering which is the “curse of dimensionality”. Hence, this encourages the study of term reduction technique in clustering bilingual documents. The objective in this study is to study the effects of reducing terms considered in clustering bilingual corpus in parallel for English and Malay documents. In this study, a genetic algorithm (GA) is used in order to reduce the number of feature selected. A single-point crossover with a crossover rate of 0.8 is used. Not only that, this study also assesses the effects of applying different mutation rate (e.g., 0.1 and 0.01) in selecting the number of features used in clustering bilingual documents. The result shows that the implementation of GA does improve the clustering mapping compared to the initial clustering mapping. Not only that, this study also discovers that GA with a mutation rate of 0.01 produces the best parallel clustering mapping results compared to GA with a mutation rate of 0.1

A PSO inspired asynchronous cooperative distributed hyper-heuristic for course timetabling problems

This paper presents a novel approach for asynchronous cooperative hyper-heuristic incorporated with particle swarm optimisation which inspired by social individual behaviour of swarm intelligence, like bird flocking and fish schooling. The proposed hyper-heuristic algorithm starts with a complete solution and tries to improve the soft constraints, whilst always remaining in the feasible region of the search space. The performances of the proposed cooperative hyper-heuristics are evaluated using the standard course timetabling benchmark problem. From the experimental results, it shows that the proposed Asynchronous Cooperative Distribute Low-level heuristics (ACDLLHs) algorithm is able to find new best solutions for all five medium problem instances and shared optimal solutions for all five small instances. When coupled with two, four and six agents, the Asynchronous Cooperative Distributed Hyper-heuristic (ACDHH) algorithm is able to improve the solution quality for a large instance

BioDARA: data summarization approach to extracting bio-medical structuring information

Problem statement: Due to the ever growing amount of biomedical datasets stored in multiple tables, Information Extraction (IE) from these datasets is increasingly recognized as one of the crucial technologies in bioinformatics. However, for IE to be practically applicable, adaptability of a system is crucial, considering extremely diverse demands in biomedical IE application. One should be able to extract a set of hidden patterns from these biomedical datasets at low cost. Approach: In this study, a new method is proposed, called Bio-medical Data Aggregation for Relational Attributes (BioDARA), for automatic structuring information extraction for biomedical datasets. BioDARA summarizes biomedical data stored in multiple tables in order to facilitate data modeling efforts in a multi-relational setting. BioDARA has the advantages or capabilities to transform biomedical data stored in multiple tables or databases into a Vector Space model, summarize biomedical data using the Information Retrieval theory and finally extract frequent patterns that describe the characteristics of these biomedical datasets. Results: the results show that data summarization performed by DARA, can be beneficial in summarizing biomedical datasets in a complex multi-relational environment, in which biomedical datasets are stored in a multi-level of one-to-many relationships and also in the case of datasets stored in more than one one-to-many relationships with non-target tables. Conclusion: This study concludes that data summarization performed by BioDARA, can be beneficial in summarizing biomedical datasets in a complex multi-relational environment, in which biomedical datasets are stored in a multi-level of one-to-many relationships