ABSTRACT
University course timetabling is a well-known problem in combinatorial optimization. When using evolutionary algorithms to solve it as a many-objective problem, measures aimed at encouraging population diversity are commonly applied in the objective value space. Difficulties can arise when the search encounters plateau regions, caused by multiple designs evaluating to a common objective vector. To address this, we propose an enhanced diversity procedure that includes genotype crowding as an additional integrated selection criterion behind dominance and phenotype diversity. We also introduce a standard form encoding to handle solution equivalence and reduce metric entropy. Four metrics and a baseline are tested across problems from the International Timetabling Competition 2007 Track 3 benchmark, using a solver based on NSGA-III. Hyper-volume is the primary performance measure. We find that genotype Hamming distance performs best. This goes against our intuition that the use of metrics closer approximating the Levenshtein distance would lead to superior performance.
- Salwani Abdullah and Hamza Turabieh. 2008. Generating University Course Timetable Using Genetic Algorithms and Local Search. International Conference on Convergence Information Technology 1 (11 2008), 254--260. Google ScholarDigital Library
- A. Alkan and Ender Özcan. 2003. Memetic Algorithms for Timetabling. 2003 Congress on Evolutionary Computation 3 (2003), 1796--1802. Google ScholarCross Ref
- Alex Bonutti, Fabio De Cesco, Luca Di Gaspero, and Andrea Schaerf. 2012. Benchmarking Curriculum-Based Course Timetabling: Formulations, Data Formats, Instances, Validation, Visualization, and Results. Annals of Operations Research 194, 1 (2012), 59--70. Google ScholarCross Ref
- Chiu-Hung Chen and Jyh-Horng Chou. 2017. Multiobjective Optimization of Airline Crew Roster Recovery Problems Under Disruption Conditions. IEEE Transactions on Systems, Man, and Cybernetics: Systems 47, 1 (2017), 133--144. Google ScholarCross Ref
- Chiu-Hung Chen, Tung-Kuan Liu, and Jyh-Horng Chou. 2013. Integrated Short-Haul Airline Crew Scheduling Using Multiobjective Optimization Genetic Algorithms. Systems, Man, and Cybernetics: Systems, IEEE Transactions on 43 (09 2013), 1077--1090. Google ScholarCross Ref
- Kalyanmoy Deb. 2001. Multiobjective Optimization Using Evolutionary Algorithms. Wiley, New York.Google ScholarDigital Library
- Kalyanmoy Deb and Himanshu Jain. 2013. NSGA III - An Evolutionary Many-Objective Optimization Algorithm Using Reference-point Based Non-dominated Sorting Approach, Part I. IEEE Transactions on Evolutionary Computation 18, 4 (2013), 577--601.Google ScholarCross Ref
- Kalyanmoy Deb and Santosh Tiwari. 2008. Omni-optimizer: A generic evolutionary algorithm for single and multi-objective optimization. European Journal of Operational Research 185, 3 (2008), 1062--1087. Google ScholarCross Ref
- Chirag Desai and Sheldon S. Williamson. 2009. Optimal design of a parallel Hybrid Electric Vehicle using multi-objective genetic algorithms. In 2009 IEEE Vehicle Power and Propulsion Conference. 871--876. Google ScholarCross Ref
- Luca di Gaspero, Andrea Schaerf, and Barry McCollum. 2007. The Second International Timetabling Competition : Curriculum-based Course Timetabling (Track 3). Electrical Engineering 3 (2007), 1--21.Google Scholar
- G.R. Greenfield. 2003. Evolving aesthetic images using multiobjective optimization. In The 2003 Congress on Evolutionary Computation, Vol. 3. 1903--1909. Google ScholarCross Ref
- Jin-Kao Hao and Una Benlic. 2011. Lower Bounds for the ITC-2007 Curriculum-Based Course Timetabling Problem. European Journal of Operational Research 212 (2011), 464--472. Google ScholarCross Ref
- Maciej Laszczyk and Paweł B. Myszkowski. 2019. Improved selection in evolutionary multi-objective optimization of multi-skill resource-constrained project scheduling problem. Information Sciences 481 (2019), 412--431. Google ScholarDigital Library
- Daniel Molina-Pérez, Edgar Alfredo Portilla-Flores, Eduardo Vega-Alvarado, Maria Bárbara Calva-Yañez, and Gabriel Sepúlveda-Cervantes. 2021. A Novel Multi-Objective Harmony Search Algorithm with Pitch Adjustment by Genotype. Applied Sciences 11, 19 (2021), 8931. Google ScholarCross Ref
- Paweł B. Myszkowski and Maciej Laszczyk. 2021. Diversity based selection for many-objective evolutionary optimisation problems with constraints. Information Sciences 546 (2021), 665--700. Google ScholarCross Ref
- R. A. Oude Vrielink, E. A. Jansen, E. W. Hans, and J. van Hillegersberg. 2019. Practices in timetabling in higher education institutions: a systematic review. Annals of Operations Research 275, 1 (2019), 145--160. Google ScholarCross Ref
- Nelishia Pillay and Ender Özcan. 2019. Automated generation of constructive ordering heuristics for educational timetabling. Annals of Operations Research 275, 1 (2019), 181--208. Google ScholarCross Ref
- James Sakal, Jonathan Fieldsend, and Edward Keedwell. 2022. Towards a Many-Objective Optimiser for University Course Timetabling. In Proceedings of 15th International Conference on Artificial Evolution. 171--184.Google Scholar
- Achkan Salehi and Stephane Doncieux. 2022. Towards QD-suite: developing a set of benchmarks for Quality-Diversity algorithms. Genetic and Evolutionary Computation Conference Companion. Google ScholarCross Ref
Index Terms
- Genotype Diversity Measures for Escaping Plateau Regions in University Course Timetabling
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