Advertisement

Extensions to a memetic timetabling system

  • Ben Paechter
  • Andrew Cumming
  • Michael G. Norman
  • Henri Luchian
Genetic Algorithms
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1153)

Abstract

This paper describes work in progress to increase the performance of a memetic timetabling system. The features looked at are two directed mutation operators, targeted mutation and a structured population that facilitates parallel implementation. Experimental results are given that show good performance improvements with directed and targeted mutation, and acceptable first results with the structure population.

Keywords

Genetic Algorithm Local Search Memetic Algorithm Soft Constraint Hard Constraint 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    Paechter, B., Luchian, H., and Cumming, A., “An Evolutionary Approach to the General Timetable Problem”, The Scientific Annals of the “Al. I. Cuza” University of Iasi, special issue for the ROSYCS symposium 1993.Google Scholar
  2. [2]
    Paechter B., Luchian H., Cumming A., and Petriuc M., “Two Solutions to the General Timetable Problem Using Evolutionary Methods”, The Proceedings of the IEEE Conference of Evolutionary Computation, 1994.Google Scholar
  3. [3]
    Paechter, B., Cumming, A., Luchian, H., “The Use of Local Search Suggestion Lists for Improving the Solution of Timetable Problems with Evolutionary Algorithms.”, Proceedings of the AISB Workshop in Evolutionary Computing, Springer-Verlag Lecture Notes in Computer Science Series No 993, Heidleberg, 1995.Google Scholar
  4. [4]
    Goldberg, D. E. Genetic Algorithms in Search, Optimisation and Machine Learning, Addison Wesley, Reading, 1989.Google Scholar
  5. [5]
    Michalewicz, Z., Genetic Algorithms + Data Structures=Evolution Programs, Springer-Verlag, Heidelberg, 1992.Google Scholar
  6. [6]
    Davis, L., Handbook of Genetic Algorithms, van Nostrand Reinhold, London, 1992.Google Scholar
  7. [7]
    Colorni, A., Dorigo M., Maniezzo, V. “Genetic Algorithms and Highly Constrained Problems: The Time-Table Case”. Parallel Problem Solving from Nature I, Goos and Hartmanis (eds.) Springer-Verlag, Heidelberg, 1990.Google Scholar
  8. [8]
    Corne, D., Ross, P. and Fang, H., “Fast Practical Evolutionary Timetabling” Proceedings of the AISB Workshop on Evolutionary Computing, Springer-Verlag Lecture Notes in Computer Science Series No. 865, Heidelberg, 1994.Google Scholar
  9. [9]
    Burke, E., Elliman D., and Weare, R., “A Genetic Algorithm for University Timetabling” AISB Workshop on Evolutionary Computing, Leeds, 1994.Google Scholar
  10. [10]
    Ross, P. and Corne, D. “Comparing Genetic Algorithms, Simulated Annealing, and Stochastic Hillclimbing on Several Real Timetable Problems”, Proceedings of the AISB Workshop in Evolutionary Computing, Springer-Verlag Lecture Notes in Computer Science Series No 993, Heidleberg, 1995.Google Scholar
  11. [11]
    Burke, E., Elliman, D. and Weare, R., “Specialised Recombinative Operators for Timetabling Problems”, Proceedings of the AISB Workshop in Evolutionary Computing, Springer-Verlag Lecture Notes in Computer Science Series No 993, Heidleberg, 1995.Google Scholar
  12. [12]
    Dawkins, R., “The Selfish Gene”, Oxford University Press, 1976Google Scholar
  13. [13]
    Moscato, P. “On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms.” Technical Report 826, Pasadena, CA, 1989.Google Scholar
  14. [14]
    Moscato, P. and Fontanari, J. F., “Stochastic versus deterministic update in simulated annealing.” Physics Letters A, 146(4):204–208, 1990Google Scholar
  15. [15]
    Norman, M.G. and Moscato, P. “A competitive-cooperative approach to complex combinatorial search”. In Selected Work for the Proceedings of the 20th Joint Conference on Informatics and Operations Research (20th JAIIO), pages 3.15–3.29, Buenos Aires, Argentina, August 1991.Google Scholar
  16. [16]
    Moscato, P. “An introduction to population approaches for optimization and hierarchical objective functions: The role of tabu search”. Annals of Operations Research, 41(1–4):85–121, 1993.Google Scholar
  17. [17]
    Moscato, P. and Norman, M.G., “A memetic approach for the travelling salesman problem. implementation of a computational ecology for combinatorial optimization on message-passing systems”. In Proceedings of the International Conference on Parallel Computing and Transputer Applications, pages 177–186, Amsterdam, IOS Press, 1992.Google Scholar
  18. [18]
    Radcliffe, N. J. and Surry P. D., “Formal Memetic Algorithms”, Proceedings of the AISB Workshop on Evolutionary Computing, Springer-Verlag Lecture Notes in Computer Science Series No. 865, Heidelberg, 1994.Google Scholar
  19. [19]
    Radcliffe, N. J. “Forma Analysis and Random Respectful Recombination” Proceedings of the Fourth International Conference on Genetic Algorithms, Morgan-Kaufmann, 1991.Google Scholar
  20. [20]
    Ross, P., Corne, D., and Fang, H., “Improving Evolutionary Timetabling with Delta Evaluation and Directed Mutation”, Parallel Problem Solving from Nature III, Springer-Verlag, Heidelberg, 1994.Google Scholar
  21. [21]
    Huberman, B.A. and Hogg, T., “Complexity and adaptation.” Physica D, 22:376–384, 1986.Google Scholar
  22. [22]
    Huberman, B.A. and Hogg, T.. “Phase transitions in artificial intelligence systems.” Artificial Intelligence, 33:155–171, 1987.Google Scholar
  23. [23]
    Muhlenbein, H., “New solutions to the mapping problem of parallel systems: The evolution approach.” Parallel Computing, 4:269, 1987.Google Scholar
  24. [24]
    Muhlenbein., H., “Evolution algorithms in combinatorial optimization”. Parallel Computing, 7:65, 1988.Google Scholar
  25. [25]
    Muhlenbein., H. “Parallel genetic algorithms, population genetics and combinatorial optimization”. In J. D. Schaffer, editor, Proceedings of the Third International Conference of Genetic Algorithms, page 416, San Mateo CA, Morgan Kaufmann, 1989Google Scholar
  26. [26]
    Brown, D., Huntley, C. L., and Spillane, A., “A parallel genetic heuristic for the quadratic assignment problem.” In J. D. Schaffer, editor, Proceedings of the Third International Conference of Genetic Algorithms, page 406, San Mateo CA, Morgan Kaufmann, 1989.Google Scholar
  27. [27]
    Gorges-Schleuter, M., “ASPARAGOS an asynchronous parallel genetic optimization strategy”. In J. D. Schaffer, editor, Proceedings of the Third International Conference of Genetic Algorithms, page 422, San Mateo CA, Morgan Kaufmann, 1989.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Ben Paechter
    • 1
  • Andrew Cumming
    • 1
  • Michael G. Norman
    • 2
  • Henri Luchian
    • 3
  1. 1.Computer Studies Dept.Napier UniversityEdinburghScotland
  2. 2.Bonnington MillMakespan Ltd.Edinburgh
  3. 3.Faculty of Computer Science“Al. I. Cuza” UniversityIasiRomania

Personalised recommendations