الفهرس 
Different Timetabling Algorithms Categories
Proposed Adaptive Ruin-Recreate Hyper- Heuristic Based ApproachOnly 14 pages are availabe for public view
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Abstract
Combinatorial optimization, by definition, is the search for an optimal configuration of a set of variables to accomplish certain goals. One of the well-known and rather complicated combinatorial optimization problems is the timetabling problem which is considered an NP-hard problem. A lot of research has been conducted in the past few decades to investigate a wide variety of algorithms and methodologies to solve timetabling combinatorial optimization problems. One of the blossoming recent methodologies in the literature is hyper-heuristics, which is a search methodology that attempts to automate the algorithm design process so that it would be able to work with different sets of problem domains.
This thesis focuses on the university course timetabling problem (UCTP) as the case of study for solving combinatorial optimization problems. Aiming to solve this problem it proposes the use of a competitive iterated local search approach strengthened with an adaptive ruin-recreate hyper-heuristic. The hyper-heuristic utilizes an online adaptive heuristic generation mechanism through a variable-sized list of add and delete operations. The algorithm was enhanced with the use of a novel approach to construct a good feasible initial solution. Moreover it was strengthened with a diversifying mechanism to allow more exploration over large search spaces to find a global rather than local near optimal solution. Additionally a weighted combination of random and previously well-behaving variables was induced in the iterated search movement operator to provide a tradeoff between diversification and intensification of the search process.
The implementation of the proposed work was experimented with the widely used ITC2007 datasets as the benchmark set. Experiments show promising results and succeed at achieving a better performance when compared to recent hyper-heuristic approaches in the literature that work on similar timetabling problems by a mean factor of 1.16.