Memetic techniques for examination timetabling

In this paper, we investigate the effectiveness of combining the main components of the memetic algorithms (MAs) on the quality of solutions produced for Uncapacitated Examination Timetabling Problem (UETP). These components are recombination, randomness, and neighbourhood structures. The Harmony Search Algorithm (HSA), which is a variation of MA, is used to perform different combinations of these components. It has three main components: Memory Consideration using the recombination, Random Consideration using the randomness and Pitch Adjustment using the neighbourhood structures (or local search). The combinations among MA components are evaluated using 17 different scenarios each of which reflects a combination of one, two or three components. The results show that the system that combines the three components (recombination, randomness, and neighbourhood structures) provides the best results. Furthermore, the best results obtained from the convergence scenarios were compared with 22 other methods that used a de facto dataset defined by Carter et al. (in Journal of the Operational Research Society 74:373–383, 1996) for UETP. The results exceed those produced by the previous methods in 2 out of 12 datasets.

[1]  Yew-Soon Ong,et al.  A proposition on memes and meta-memes in computing for higher-order learning , 2009, Memetic Comput..

[2]  Christian Blum,et al.  Metaheuristics in combinatorial optimization: Overview and conceptual comparison , 2003, CSUR.

[3]  Yew-Soon Ong,et al.  Memetic Computation—Past, Present & Future [Research Frontier] , 2010, IEEE Computational Intelligence Magazine.

[4]  Luca Di Gaspero,et al.  Tabu Search Techniques for Examination Timetabling , 2000, PATAT.

[5]  Sanja Petrovic,et al.  A time-predefined local search approach to exam timetabling problems , 2004 .

[6]  Tonghua Zhang,et al.  Overview of Applications and Developments in the Harmony Search Algorithm , 2009 .

[7]  Peter J. Stuckey,et al.  A Hybrid Algorithm for the Examination Timetabling Problem , 2002, PATAT.

[8]  Luís Paquete,et al.  Empirical Analysis of Tabu Search for the Lexicographic Optimization of the Examination Timetabling Problem , 2002 .

[9]  Mohammed Azmi Al-Betar,et al.  A Harmony Search with Multi-pitch Adjusting Rate for the University Course Timetabling , 2010, Recent Advances In Harmony Search Algorithm.

[10]  Sanja Petrovic,et al.  A Novel Similarity Measure for Heuristic Selection in Examination Timetabling , 2004, PATAT.

[11]  M. Mahdavi,et al.  ARTICLE IN PRESS Available online at www.sciencedirect.com , 2007 .

[12]  Zong Woo Geem,et al.  A New Heuristic Optimization Algorithm: Harmony Search , 2001, Simul..

[13]  Graham Kendall,et al.  A Tabu Search Hyper-heuristic Approach to the Examination Timetabling Problem at the MARA University of Technology , 2004, PATAT.

[14]  Chee Keong Kwoh,et al.  Feasibility Structure Modeling: An Effective Chaperone for Constrained Memetic Algorithms , 2010, IEEE Transactions on Evolutionary Computation.

[15]  Edmund K. Burke,et al.  A survey of search methodologies and automated system development for examination timetabling , 2009, J. Sched..

[16]  Edmund K. Burke,et al.  Adaptive automated construction of hybrid heuristics for exam timetabling and graph colouring problems , 2009, Eur. J. Oper. Res..

[17]  Sanja Petrovic,et al.  A graph-based hyper-heuristic for educational timetabling problems , 2007, Eur. J. Oper. Res..

[18]  R. Sabourin,et al.  Application of a hybrid multi-objective evolutionary algorithm to the uncapacitated exam proximity problem , 2004 .

[19]  Gilbert Laporte,et al.  Examination Timetabling: Algorithmic Strategies and Applications , 1994 .

[20]  Jonathan M. Thompson,et al.  GRASPing the Examination Scheduling Problem , 2002, PATAT.

[21]  Daniel Brélaz,et al.  New methods to color the vertices of a graph , 1979, CACM.

[22]  Moshe Dror,et al.  Investigating Ahuja–Orlin’s large neighbourhood search approach for examination timetabling , 2007, OR Spectr..

[23]  Giuseppe F. Italiano,et al.  Novel Local-Search-Based Approaches to University Examination Timetabling , 2008, INFORMS J. Comput..

[24]  Jing Tang,et al.  Diversity-adaptive parallel memetic algorithm for solving large scale combinatorial optimization problems , 2006, Soft Comput..

[25]  Yew-Soon Ong,et al.  A Probabilistic Memetic Framework , 2009, IEEE Transactions on Evolutionary Computation.

[26]  Sanja Petrovic,et al.  Hybrid variable neighbourhood approaches to university exam timetabling , 2010, Eur. J. Oper. Res..

[27]  Mohammed Azmi,et al.  A hybrid harmony search for university course timetabling , 2009 .

[28]  Edmund K. Burke,et al.  Solving Examination Timetabling Problems through Adaption of Heuristic Orderings , 2004, Ann. Oper. Res..

[29]  Natalio Krasnogor,et al.  Adaptive Cellular Memetic Algorithms , 2009, Evolutionary Computation.

[30]  Edmund K. Burke,et al.  Enhancing Timetable Solutions with Local Search Methods , 2002, PATAT.

[31]  Graham Kendall,et al.  Monte Carlo hyper-heuristics for examination timetabling , 2012, Ann. Oper. Res..

[32]  Zong Woo Geem,et al.  An analysis of selection methods in memory consideration for harmony search , 2013, Appl. Math. Comput..

[33]  Hishammuddin Asmuni,et al.  An investigation of fuzzy multiple heuristic orderings in the construction of university examination timetables , 2009, Comput. Oper. Res..

[34]  Edmund K. Burke,et al.  Hybridizations within a graph-based hyper-heuristic framework for university timetabling problems , 2009, J. Oper. Res. Soc..

[35]  Mohammed Azmi Al-Betar,et al.  University Course Timetabling Using a Hybrid Harmony Search Metaheuristic Algorithm , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[36]  Xin-She Yang.  Harmony Search as a Metaheuristic Algorithm , 2009 .

[37]  Andy J. Keane,et al.  Meta-Lamarckian learning in memetic algorithms , 2004, IEEE Transactions on Evolutionary Computation.

[38]  Efthymios Housos,et al.  An improved multi-staged algorithmic process for the solution of the examination timetabling problem , 2012, Ann. Oper. Res..

[39]  Ben Paechter,et al.  Setting the Research Agenda in Automated Timetabling: The Second International Timetabling Competition , 2010, INFORMS J. Comput..

[40]  Zong Woo Geem,et al.  Music-Inspired Harmony Search Algorithm , 2009 .

[41]  Mohammed Azmi Al-Betar,et al.  A harmony search algorithm for university course timetabling , 2010, Annals of Operations Research.

[42]  Mohammed A. Awadallah,et al.  Novel selection schemes for harmony search , 2012, Appl. Math. Comput..

[43]  Michael Eley,et al.  Ant Algorithms for the Exam Timetabling Problem , 2006, PATAT.

[44]  Hishammuddin Asmuni,et al.  Fuzzy Multiple Heuristic Orderings for Examination Timetabling , 2004, PATAT.

[45]  Kevin Kok Wai Wong,et al.  Classification of adaptive memetic algorithms: a comparative study , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[46]  Wolfgang Banzhaf,et al.  A study of heuristic combinations for hyper-heuristic systems for the uncapacitated examination timetabling problem , 2009, Eur. J. Oper. Res..