Some observations about GA-based exam timetabling

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1408, Issue , 1998, Pages 115-129

  Ross, Peter ^a   Hart, Emma ^a   Corne, Dave ^b  

^a UNIVERSITY OF EDINBURGH   (United Kingdom)

^b UNIVERSITY OF READING   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

SCHEDULING;

CONSTRAINED PROBLEM; DIRECT ENCODING; EXAM TIMETABLING; GENETIC ALGORITHM (GAS); GRAPH COLOURING; NON-EVOLUTIONARY APPROACH; SUB-PROBLEMS;

GENETIC ALGORITHMS;

EID: 84956968077     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/bfb0055884     Document Type: Conference Paper

References (27)

1. Michael G. Norman Ben Paechter, Andrew Cumming and Henri Luchian. Extensions to a memetic timetabling system. [4].
2. Daniel Brelaz. New methods to color the vertices of a graph. Communications of the ACM, 22(4):251-256, April 1979.
3. E. Burke, J. Newell, and R. Weare. A memetic algorithm for university exam timetabling. In The Practice and Theory of Automated Timetabting [4], pages 241-250.
4. E. Burke and P.M. Ross. The Practice and Theory of Automated Timetabling. LNCS 1153. Springer-Verlag, Heidelberg, October 1996.
5. M. Carter, G. Laporte, and S. Y. Lee. Examination timetabling: algorithmic strategies and applications. Journal of the Operational Research Society, 47(3):373-383, 1996.
6. A Colorni, M Dorigo, and V Maniezzo. Metaheuristics for high-school timetabling. Computational Optimization and Applications, 9(2), 1998. in press.
7. Dave Come and Peter Ross. Some combinatorial landscapes on which a genetic algorithm outperforms other stochastic iterative methods. In T. Fogarty, editor, Evolutionary Computing: AISB Workshop, Sheffield 1995, Selected Papers, LNCS 993. Springer-Verlag, 1995.
8. Dave Corne and Peter Ross. Peckish initialisation strategies for evolutionary timetabling. [4], pages 227-240.
9. Dave Corne, Peter Ross, and Hsiao-Lan Fang. Ga research note 7: Fast practical evolutionary timetabling. Technical report, University of Edinburgh Department of Artificial Intelligence, 1993.
10. Dave Come, Peter Ross, and Hsiao-Lan Fang. Fast practical evolutionary timetabling. In Terry C. Fogarty, editor, Selected Papers: AISB Workshop on Evolu. tionary Computing, Lecture Notes in Computer Science No 865, pages 250-263. Springer Verlag, 1994.
11. Dave Corne, Peter Ross, and Hsiao-Lan Fang. Evolutionary timetabling: Practice, prospects and work in progress. In P. Prosser, editor, Proceedings of 13th UK Planning SIG. University of Strathclyde, 1994.
12. K. Dowsland. Genetic algorithms - a tool for or? Journal of the Operational Research Society, 47(3):550-561, 1996.
13. A. Erguöl. Ga-based examination scheduling experience at a middle east technical university. [4], pages 212-227.
14. Larry J. Eshelman. The chc adaptive search algorithm: How to have safe search when engaging in nontraditional genetic recombination. In G. Rawlins, editor, Foundations of Genetic Algorithms, pages 265-283. Morgan Kanfmann, 1991.
15. V. Gordon, A. Bohm, and D. Whitley. A note on the performance of genetic algorithms on zero-one knapsack problems. Technical Report CS-93-108, Colorado State University Dept of Computer Science, 1993.
16. Tad Hogg, Bernado A. Huberman, and Colin P. Williams. Phase transitions and the search problem. Artificial Intelligence, 81(1-2):1-15, 1996.
17. Hugo Terashima Marin. A comparison of ga-based methods and graph-colouring methods for solving the timetabling problem. Master’s thesis, Department of AI, University of Edinburgh, 1994.
18. Patrick Prosser. An empirical study of phase transitions in binary constraint satisfaction problems. Artificial Intelligence, 81(1-2):81-109, 1996.
19. Peter Ross and Dave Come. Solving large multi-constrained multi-objective timetabling problems with stochastic iterative search strategies. (journal paper, in preparation).
20. Peter Ross and Dave Come. Comparing genetic algorithms, simulated annealing, and stochastic hillclimbing on timetabling problems. In T. Fogarty, editor, Evolutionary Computing: AISB Workshop, Sheffield 1995, Selected Papers, LNCS 993. Springer-Verlag, 1995.
21. Peter Ross and Dave Corne. The phase transition niche for evolutionary algorithms in timetabling. [4], pages 309-324.
22. Peter Ross, Dave Corne, and Hsiao-Lan Fang. Improving evolutionary timetabling with delta evaluation and directed mutation. In Y. Davidor nd H-P. Schwefel and R. Manner, editors, Parallel Problem-solving from Nature - PPSN III, LNCS, pages 566-565. Springer-Verlag, 1994.
23. Peter Ross, Dave Corne, and Hsiao-Lan Fang. Successful lecture timetabling with evolutionary algorithms. In A.E.Eiben, B.Manderick, and Zs.Ruttkay, editors, ECAI-94 Workshop W17: Applied Genetic and other Evolutionary Algorithms. ECAI-94, 1994.
24. J. D. Schaffer and L. J. Eshelman. Combinatorial optimization by genetic algorithms: the value of the genotype/phenotype distinction. In V. Rayward-Smith, I. H. Osman, C. R. Reeves, and G. D. Smith, editors, Modern Heuristic Search Methods, New York, 1996. John Wiley and Sons.
25. Barbara M. Smith and Martin E. Dyer. Locating the phase transitions in binary constraint satisfcation problems. Artificial Intelligence, 81(1-2):155-181, 1996.
26. J. Thompson and K. Dowsland. General cooling schedules for a simulated annealing based timetabling system. [4], pages 345-363.
27. P.A. Turner. Genetic algorithms and multiple distinct solutions. Master’s thesis, Department of AI, University of Edinburgh, 1994.