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This paper presents a memetic multiobjective optimization algorithm based on NNIA for examination timetabling problems. In this paper, the examination timetabling problem is considered as a two-objective optimization problem while it is modeled as a single-objective optimization problem generally. Within the NNIA framework, the special crossover op...
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Citations
... If the number of courses increases, the opportunity is not scheduled at the same time, and the subject with the highest number of degrees will be scheduled in advance. Pre-scheduled courses minimize or avoid the existence of a timeslot that contains several courses taken by the same student [17]. ...
Scheduling exams in colleges are a complicated job that is difficult to solve conventionally. Exam timetabling is one of the combinatorial optimization problems where there is no exact algorithm that can answer the problem with the optimum solution and minimum time possible. This study investigated the University of Toronto benchmark dataset, which provides 13 real instances regarding the scheduling of course exams from various institutions. The hard constraints for not violate the number of time slots must be fulfilled while paying attention to fitness and running time. Algorithm of largest degree, hill climbing, and tabu search within a hyper-heuristic framework is investigated with regards to each performance. This study shows that the Tabu search algorithm produces much lower penalty value for all datasets by reducing 18-58% from the initial solution.
... Pada penelitian yang dilakukan oleh Lei dkk (2017) examination timetabling problem diselesaikan dengan menggunakan Multiobjective Optimization Evolutionary Algorithms (MOEAs) dalam kerangka Nondominated Neighbor Immune Algorithm (NNIA). Hasil penelitian menunjukkan algoritma yang digunakan dapat menghasilkan jadwal yang relatif bebas dari konflik dan solusi yang berbeda-beda sehingga pembuat keputusan dapat memilih sesuai preferensi [9]. Selain itu, ada penelitian yang sudah dilakukan oleh Al-Betar (2020) dengan menggunakan algoritma Hill Climbing yang dioptimasi yaitu algoritma β-Hill Climbing. ...
Examination Timetabling Problem is one of the optimization and combinatorial problems. It is proved to be a non-deterministic polynomial (NP)-hard problem. On a large scale of data, the examination timetabling problem becomes a complex problem and takes time if it solved manually. Therefore, heuristics exist to provide reasonable enough solutions and meet the constraints of the problem. In this study, a real-world dataset of Examination Timetabling (Toronto dataset) is solved using a Hill-Climbing and Tabu Search algorithm. Different from the approach in the literature, Tabu Search is a meta-heuristic method, but we implemented a Tabu Search within the hyper-heuristic framework. The main objective of this study is to provide a better understanding of the application of Hill-Climbing and Tabu Search in hyper-heuristics to solve timetabling problems. The results of the experiments show that Hill-Climbing and Tabu Search succeeded in automating the timetabling process by reducing the penalty 18-65% from the initial solution. Besides, we tested the algorithms within 10,000-100,000 iterations, and the results were compared with a previous study. Most of the solutions generated from this experiment are better compared to the previous study that also used Tabu Search algorithm.
For many investors, investing in the capital market can be quite challenging. It involves the task of accurately selecting and allocating funds to different shares in order to create an optimal portfolio. The problem of determining the ideal combination and proportion of shares within the portfolio can be addressed through the utilization of a genetic algorithm method approach. Portfolio optimization is the process of strategically selecting the most advantageous combination of investments that offers the highest potential return for a given level of risk undertaken by investors an optimal portfolio is one that maximizes the Sharpe ratio, which measures the potential return an investment can yield in relation to the risk assumed by investors. This study explores the application of genetic algorithms, a class of optimization algorithms, to enhance portfolio optimization. Genetic algorithms offer a powerful approach to determining the most favorable investment portfolios based on various criteria, including profit maximization, risk reduction, and management of asset correlations. The research critically highlights the distinct advantages of utilizing genetic algorithms over conventional techniques for portfolio optimization. Moreover, it introduces a comprehensive method to assess the effectiveness of genetic algorithms in portfolio optimization.
