Joe Henry Obit

Great deluge with non-linear decay rate for solving course timetabling problems

Course timetabling is the process of allocating, subject to constraints, limited rooms and timeslots for a set of courses to take place. Usually, in addition to constructing a feasible timetable (all constraints satisfied), there are desirable goals like minimising the number of undesirable allocations (e.g. courses timetabled in the last timeslot of the day). The construction of course timetables is regarded as a complex problem common to a wide range of educational institutions. The great deluge algorithm explores neighbouring solutions which are accepted if they are better than the best solution so far or if the detriment in quality is no larger than the current water level. In the original great deluge, the water level decreases steadily in a linear fashion. In this paper, we propose a modified version of the great deluge algorithm in which the decay rate of the water level is non-linear. The proposed method produces new best results in 4 of the 11 course timetabling problem instances used in our experiments.

Evolutionary Non-linear Great Deluge for University Course Timetabling

This paper presents a hybrid evolutionary algorithm to tackle university course timetabling 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. That initialisation process is capable of producing feasible solutions even for the 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 conduct experiments to evaluate the performance of the proposed hybrid algorithm and in particular, the contribution of the evolutionary operators. Our results show that the hybrid between non-linear great deluge and evolutionary operators produces very good results on the instances of the university course timetabling problem tackled here.

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.

A ruled-based part of speech (RPOS) tagger for malay text articles

The Malay language is an Austronesian language spoken in most countries in the South East Asia region that includes Malaysia, Indonesia, Singapore, Brunei and Thailand. Traditional linguistics is well developed for Malay but there are very limited resources and tools that are available or made accessible for computer linguistic analysis of Malay language. Assigning part of speech (POS) to running words in a sentence for Malay language is one of the pipeline processes in Natural Language Processing (NLP) tasks and it is not well investigated. This paper outlines an approach to perform the Part of Speech (POS) tagging for Malay text articles. We apply a simple Rule-based Part of Speech (RPOS) tagger to perform the tagging operation on Malay text articles. POS tagging can be described as a task of performing automatic annotation of syntactic categories for each word in a text document. A rule-based POS tagger generally involves a POS tag dictionary and a set of rules in order to identify the words that are considered parts of speech. In this paper, we propose a framework that applies Malay affixing rules to identify the Malay POS tag and the relation between words in order to select the best POS tag for words that have two or more valid POS tags. The results show that the performance accuracy of the ruled-based POS tagger is higher compared to a statistical POS tagger. This indicates that the proposed RPOS tagger is able to predict any unknown words POS at some promising accuracy.

Computational Study of Non-linear Great Deluge for University Course Timetabling

The great deluge algorithm explores neighbouring solutions which are accepted if they are better than the best solution so far or if the detriment in quality is no larger than the current water level. In the original great deluge method, the water level decreases steadily in a linear fashion. In this paper,we conduct a computational study of a modified version of the great deluge algorithm in which the decay rate of the water level is non-linear. For this study, we apply the non-linear great deluge algorithm to difficult instances of the university course timetabling problem. The results presented here show that this algorithm performs very well compared to other methods proposed in the literature for this problem. More importantly, this paper aims to better understand the role of the non-linear decay rate on the behaviour of the non-linear great deluge approach.

The Study of Genetic Algorithm Approach to Solving University Course Timetabling Problem

This research presents the metaheuristic strategy to solve educational timetabling problem. The metaheuristic described in this research highlight the role of Genetic Algorithm (GA) when the algorithm improves the quality of solution by performing genetic operators. Two datasets of university course timetabling are used whereby the datasets are obtained from Universiti Malaysia Sabah Labuan International Campus (UMSLIC). The research experiment is conducted by comparing the quality of solutions produced by Genetic Algorithm with other metaheuristics which have been done in the past researches. The experimental results suggest that Genetic Algorithm manages to produces good solutions in this domain although other algorithms are able to improve the quality of the solutions.

Factors Affecting Sentiment Prediction of Malay News Headlines Using Machine Learning Approaches

Most sentiment analysis researches are done with the help of supervised machine learning techniques. Analyzing sentiment for these English text reviews is a non-trivial task in order to gauge public perception and acceptance of a particular issue being addressed. Nevertheless, there are not many studies conducted on analyzing sentiment of Malay news headlines due to lack of resources and tools. The Malay news headlines normally consist of a few words and are often written with creativity to attract the readers’ attention. This paper proposes a standard framework that investigates factors affecting sentiment prediction of Malay news headlines using machine learning approaches. It is important to investigate factors (e.g., types of classifiers, proximity measurements and number of Nearest Neighbors, k) that influence the prediction performance of the sentiment analysis as it helps to study and understand the parameters that can be tuned to optimize the prediction performance. Based on the results obtained, Support Vector Machine and Naive Bayes classifiers were capable to obtain higher accuracy compared to the k-Nearest Neighbors (k-NN) classifier. In term of proximity measurement and number of Nearest Neighbors, k, the k-NN classifier achieved higher prediction performance when the Cosine similarity is applied with a small value of k (e.g., 3 and 5), compared to the Euclidean distance because it measures can be affected by the high dimensionality of the data.

Assessing Factors that Influence the Performances of Automated Topic Selection for Malay Articles

Malay language is a major language that is in used by citizens of Malaysia, Indonesia, Singapore and Brunei. As the language is widely used, there are abundant of text articles written in Malay language that are available on the internet. This has resulted in the increasing of the Malay articles published online and the number of articles has increased greatly over the years. Automatically labeling Malay text articles is crucial in managing these articles. Due to lack of resources and tools used to perform the topic selection automatically for Malay text articles, this paper studies the factors that influence the performances of the algorithms that can be applied to perform a topic selection automatically for Malay articles. This is done by comparing the contents of the articles with the corresponding topics and all Malay articles will be assigned to the appropriate topics depending on the results of the classification process. In this paper, all Malay articles will be classified by using the k-Nearest Neighbors (k-NN) and Naive Bayes classifiers. Both classifiers are used to classify and assign a topic to these Malay articles according to a predefined set of topics. The effectiveness of classifying these Malay articles using the k-NN classifier is highly dependent on the distance methods used and the number of Nearest Neighbors, k. Thus, this paper also assesses the effects of using different distance methods (e.g., Cosine Similarity and the Euclidean Distance) and varying the number of clusters, k. Other than that, the effects of utilizing the stemming process on the performance of the classifiers are also studied. Based on the results obtained, the proposed approach shows that the k-NN classifier performs better than the Naive Bayes classifier in classifying the Malay articles into their respective topics. In addition to that, the stemming process also improves the overall performances of both classifiers. Other findings include the application of Cosine Similarity as the distance measure has improved the performance of the k-NN classifier.

Agent based integer programming framework for solving real-life curriculum-based university course timetabling

This research proposes an agent-based framework for solving reallife curriculum-based University Course Timetabling problems (CB-UCT) at the Universiti Malaysia Sabah, Labuan International Campus (UMSLIC). Similar to other timetabling problems, CB-UCT in UMSLIC has its own distinctive constraints and features. The proposed framework deal with the problem using a distributed Multi-Agent System (MAS) environment in which a central agent coordinates various IP agents that cooperate by sharing the best part of the solution and direct the IP agents towards more promising search space and hence improve a common global list of the solutions. All agents are incorporated with Integer programming (IP) search methodology, which is used to generate initial solution in this, regards as well. We discuss how sequential IP search methodology can be incorporated into the proposed multi-agent approach in order to conduct parallel search for CB-UCT. The agent-based IP is tested over two real-life datasets, semester 1 session 2016/2017 and semester 2 session 2016/2017. The experimental results show that the agent-based IP is able to improve the solution generated by the sequential counterpart for UMSLIC’s problem instance used in the current study impressively by 12.73% and 17.89% when three and six IP agents are used respectively. Moreover, the experiment also shows that increasing the number of IP agents lead to the better results.

An Investigation towards Hostel Space Allocation Problem with Stochastic Algorithms

This research presents the study of stochastic algorithms in one of the limited study in Space Allocation Problem. The domain involves the allocation of students into the available rooms which is known as Hostel Space Allocation Problem. The problem background of this domain which related with hard constraints and soft constraints are discussed and the formal mathematical models of constraints in Universiti Malaysia Sabah Labuan International Campus are presented. The construction of initial solution is handled by Constraint Programming algorithm. Two algorithms mainly Great Deluge with linear and non-linear decay rate and Simulated Annealing with linear reduction are proposed to improve the quality of solution. The experimental results show that Simulated Annealing with linear reduction temperature performs well in this domain.