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Memes, Self-generation and Nurse Rostering

  • Conference paper
Book cover Practice and Theory of Automated Timetabling VI (PATAT 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3867))

Abstract

This paper presents an empirical study on memetic algorithms in two parts. In the first part, the details of the memetic algorithm experiments with a set of well known benchmark functions are described. In the second part, a heuristic template is introduced for solving timetabling problems. Two adaptive heuristics that utilize a set of constraint-based hill climbers in a co-operative manner are designed based on this template. A hyper-heuristic is a mechanism used for managing a set of low-level heuristics. At each step, an appropriate heuristic is chosen and applied to a candidate solution. Both adaptive heuristics can be considered as hyper-heuristics. Memetic algorithms employing each hyper-heuristic separately as a single hill climber are experimented on a set of randomly generated nurse rostering problem instances. Moreover, the standard genetic algorithm and two self-generating multimeme memetic algorithms are compared to the proposed memetic algorithms and a previous study.

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References

  1. Ackley, D.: An empirical study of bit vector function optimization. In: Davis, L. (ed.) Genetic Algorithms and Simulated Annealing, pp. 170–215. Pitman, London (1987)

    Google Scholar 

  2. Ahmad, J., Yamamoto, M., Ohuchi, A.: Evolutionary algorithms for nurse scheduling problem. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 196–203 (2000)

    Google Scholar 

  3. Aickelin, U., Bull, L.: On the application of hierarchical coevolutionary genetic algorithms: recombination and evaluation partners. Journal of Applied Systems Studies 4, 2–17 (2003)

    Google Scholar 

  4. Aickelin, U., Dowsland, K.: An indirect genetic algorithm for a nurse scheduling problem. Computers and Operations Research 31, 761–778 (2003)

    Article  Google Scholar 

  5. Alkan, A., Özcan, E.: Memetic algorithms for timetabling. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 1796–1802 (2003)

    Google Scholar 

  6. Berrada, I., Ferland, J., Michelon, P.: A multi-objective approach to nurse scheduling with both hard and soft constraints. Socio-Economic Planning Science 30, 183–193 (1996)

    Article  Google Scholar 

  7. Burke, E.K., Cowling, P.I., De Causmaecker, P., Vanden Berghe, G.: A memetic approach to the nurse rostering problem. Applied Intelligence 15, 199–214 (2001)

    Article  MATH  Google Scholar 

  8. Burke, E.K., De Causmaecker, P., Petrovic, S., Vanden Berghe, G.: Variable neighbourhood search for nurse rostering problems. In: Resende, M.G.C., de Sousa, J.P. (eds.) Metaheuristics: Computer Decision-Making, ch. 7, pp. 153–172. Kluwer, Dordrecht (2003)

    Google Scholar 

  9. Burke, E.K., De Causmaecker, P., Vanden Berghe, G.: A hybrid tabu search algorithm for the nurse rostering problem. In: McKay, B., Yao, X., Newton, C.S., Kim, J.-H., Furuhashi, T. (eds.) SEAL 1998. LNCS (LNAI), vol. 1585, pp. 187–194. Springer, Heidelberg (1999)

    Chapter  Google Scholar 

  10. Burke, E.K., De Causmaecker, P., Vanden Berghe, G., Van Landeghem, H.: The state of the art of nurse rostering. Journal of Scheduling 7, 441–499 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  11. Burke, E.K., Kendall, G., Newall, J., Hart, E., Ross, P., Schulenburg, S.: Hyper-heuristics: an emerging direction in modern search technology. In: Glover, F., Kochenberger, G. (eds.) Handbook of Metaheuristics, pp. 457–474. Kluwer, Dordrecht (2003)

    Chapter  Google Scholar 

  12. Burke, E.K., Kendall, G., Soubeiga, E.: A tabu-search hyperheuristic for timetabling and rostering. Journal of Heuristics 9, 451–470 (2003)

    Article  Google Scholar 

  13. Chun, A.H.W., Chan, S.H.C., Lam, G.P.S., Tsang, F.M.F., Wong, J., Yeung, D.W.M.: Nurse rostering at the Hospital Authority of Hong Kong. In: Proceedings of the 17th National Conference on AAAI and 12th Conference on IAAI, pp. 951–956 (2000)

    Google Scholar 

  14. Cowling, P., Kendall, G., Soubeiga, E.: A hyper-heuristic approach to scheduling a sales summit. In: Burke, E., Erben, W. (eds.) PATAT 2000. LNCS, vol. 2079, pp. 176–190. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  15. Davis, L.: The Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York (1991)

    Google Scholar 

  16. Davis, L.: Bit climbing, representational bias, and test suite design. In: Proceedings of the 4th International Conference on Genetic Algorithms, pp. 18–23 (1991)

    Google Scholar 

  17. De Jong, K.: An analysis of the behaviour of a class of genetic adaptive systems. Ph.D. Thesis, University of Michigan, Ann Arbor, MI (1975)

    Google Scholar 

  18. Dowsland, K.: Nurse scheduling with tabu search and strategic oscillation. European Journal of Operations Research 106, 393–407 (1998)

    Article  MATH  Google Scholar 

  19. Duenas, A., Mort, N., Reeves, C., Petrovic, D.: Handling preferences using genetic algorithms for the nurse scheduling problem. In: MISTA 2003. Proceedings of the 1st Multidisciplinary International Conference on Scheduling: Theory and Applications, Nottingham, vol. 1, pp. 180–196 (August 2003)

    Google Scholar 

  20. Easom, E.E.: A survey of global optimization techniques. M.Eng. Thesis, University of Louisville, KY (1990)

    Google Scholar 

  21. Even, S., Itai, A., Shamir, A.: On the complexity of timetable and multicommodity flow problems. SIAM Journal of Computing 5, 691–703 (1976)

    Article  MATH  MathSciNet  Google Scholar 

  22. Fang, H.L.: Genetic algorithms in timetabling and scheduling. Ph.D. Thesis, Department of Artificial Intelligence, University of Edinburgh, Scotland (1994)

    Google Scholar 

  23. Gendreau, M., Buzon, I., Lapierre, S., Sadr, J., Soriano, P.: A tabu search heuristic to generate shift schedules. In: MISTA 2003. Proceedings of the 1st Multidisciplinary International Conference on Scheduling: Theory and Applications, Nottingham, vol. 2, pp. 526–528 (August 2003)

    Google Scholar 

  24. Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, MA (1989)

    MATH  Google Scholar 

  25. Goldberg, D.E.: Genetic algorithms and Walsh functions: part I, a gentle introduction. Complex Systems 3, 129–152 (1989)

    MATH  MathSciNet  Google Scholar 

  26. Goldberg, D.E.: Genetic algorithms and Walsh functions: part II, deception and its analysis. Complex Systems 3, 153–171 (1989)

    MATH  MathSciNet  Google Scholar 

  27. Griewangk, A.O.: Generalized descent of global optimization. Journal of Optimization Theory and Applications 34, 11–39 (1981)

    Article  MathSciNet  Google Scholar 

  28. Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, MI (1975)

    Google Scholar 

  29. Han, L., Kendall, G.: Application of genetic algorithm based hyper-heuristic to personnel scheduling problems. In: Kendall, G., Burke, E.K., Petrovic, S., Gendreau, M. (eds.) MISTA 2003. Proceedings of the 1st Multidisciplinary International Conference on Scheduling: Theory and Applications, Nottingham, August 2003, pp. 528–537. Springer, Berlin (2005)

    Google Scholar 

  30. Kawanaka, H., Yamamoto, K., Yoshikawa, T., Shinogi, T., Tsuruoka, S.: Genetic algorithms with the constraints for nurse scheduling problem. In: Proceedings of IEEE Congress on Evolutionary Computation, CEC, Seoul, pp. 1123–1130 (2001)

    Google Scholar 

  31. Krasnogor, N.: Studies on the theory and design space of memetic algorithms. Ph.D. Thesis, University of the West of England, Bristol, UK (2002)

    Google Scholar 

  32. Krasnogor, N., Smith, J.E.: Multimeme algorithms for the structure prediction and structure comparison of proteins. In: GECCO 2002. Proceedings of the Bird of a Feather Workshops, pp. 42–44 (2002)

    Google Scholar 

  33. Krasnogor, N., Smith, J.E.: Emergence of profitable search strategies based on a simple inheritance mechanism. In: GECCO 2001. Proceedings of the Genetic and Evolutionary Computation Conference, pp. 432–439 (2001)

    Google Scholar 

  34. Krasnogor, N., Smith, J.E.: A memetic algorithm with self-adaptive local search: TSP as a case study. In: GECCO 2000. Proceedings of the Genetic and Evolutionary Computation Conference, pp. 987–994 (2000)

    Google Scholar 

  35. Leighton, F.T.: A graph coloring algorithm for large scheduling problems. Journal of Research of the National Bureau of Standards 84, 489 (1979)

    MATH  MathSciNet  Google Scholar 

  36. Li, H., Lim, A., Rodrigues, B.: A hybrid AI approach for nurse rostering problem. In: Proceedings of the 2003 ACM Symposium on Applied Computing, pp. 730–735 (2003)

    Google Scholar 

  37. Mitchell, M., Forrest, S.: Fitness landscapes: royal road functions. In: Baeck, T., Fogel, D., Michalewicz, Z. (eds.) Handbook of Evolutionary Computation, Institute of Physics Publishing, Bristol, and Oxford University Press, Oxford (1997)

    Google Scholar 

  38. Moscato, P., Norman, M.G.: A memetic approach for the traveling salesman problem implementation of a computational ecology for combinatorial optimization on message-passing systems. In: Valero, M., Onate, E., Jane, M., Larriba, J.L., Suarez, B. (eds.) Parallel Computing and Transputer Applications, pp. 177–186. IOS Press, Amsterdam (1992)

    Google Scholar 

  39. Ning, Z., Ong, Y.S., Wong, K.W., Lim, M.H.: Choice of memes in memetic algorithm. In: Proceedings of the 2nd International Conference on Computational Intelligence, Robotics and Autonomous Systems (2003)

    Google Scholar 

  40. Ong, Y.S., Keane, A.J.: Meta-Lamarckian learning in memetic algorithms. IEEE Transactions on Evolutionary Computation 8, 99–110 (2004)

    Article  Google Scholar 

  41. Özcan, E.: Memetic Algorithms for Nurse Rostering. In: Yolum, p., Güngör, T., Gürgen, F., Özturan, C. (eds.) ISCIS 2005. LNCS, vol. 3733, pp. 482–492. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  42. Özcan, E.: Towards an XML based standard for timetabling problems: TTML. In: Kendall, G., Burke, E.K., Petrovic, S., Gendreau, M. (eds.) MISTA 2003. Proceedings of the 1st Multidisciplinary International Conference on Scheduling: Theory and Applications, Nottingham, p. 163. Springer, Berlin (August 2005)

    Google Scholar 

  43. Özcan, E., Bilgin, B., Korkmaz, E.E.: Hill climbers and mutational heuristics in hyperheuristics. In: Runarsson, T.P., Beyer, H.-G., Burke, E., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds.) Parallel Problem Solving from Nature - PPSN IX. LNCS, vol. 4193, pp. 202–211. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  44. Özcan, E., Ersoy, E.: Final exam scheduler – FES. In: Proceedings of the 2005 IEEE Congress on Evolutionary Computation, vol. 2, pp. 1356–1363 (2005)

    Google Scholar 

  45. Özcan, E., Onbasioglu, E.: Memetic algorithms for parallel code optimization. International Journal of Parallel Programming 35, 33–61 (2007)

    Article  MATH  Google Scholar 

  46. Radcliffe, N.J., Surry, P.D.: Formal memetic algorithms. In: Fogarty, T.C. (ed.) Evolutionary Computing. LNCS, vol. 865, pp. 1–16. Springer, Heidelberg (1994)

    Google Scholar 

  47. Rastrigin, L.A.: Extremal Control Systems, Theoretical Foundations of Engineering Cybernetics Series, Nauka, Moscow (1974)

    Google Scholar 

  48. Ross, P., Corne, D., Fang, H.-L.: Improving evolutionary timetabling with delta evaluation and directed mutation. In: Davidor, Y., Männer, R., Schwefel, H.-P. (eds.) Parallel Problem Solving from Nature - PPSN III. LNCS, vol. 866, pp. 556–565. Springer, Heidelberg (1994)

    Google Scholar 

  49. Ross, P., Corne, D., Fang, H.-L.: Fast practical evolutionary timetabling. In: Proceedings of the AISB Workshop on Evolutionary Computation, pp. 250–263 (1994)

    Google Scholar 

  50. Schwefel, H.-P.: Numerical Optimization of Computer Models. Wiley, Chichester (1981)

    MATH  Google Scholar 

  51. Schwefel, H.-P.: Evolution and Optimum Seeking. Wiley, New York (1995)

    Google Scholar 

  52. Smith, J., Fogarty, T.C.: Operator and parameter adaptation in genetic algorithms. Soft Computing 1, 81–87 (1997)

    Article  Google Scholar 

  53. Tasoulis, D., Pavlidis, N., Plagianakos, V., Vrahatis, M.: Parallel differential evolution. In: Proceedings of the 2004 IEEE Congress on Evolutionary Computation, pp. 2023–2029. IEEE Computer Society Press, Los Alamitos (2004)

    Chapter  Google Scholar 

  54. Whitley, D.: Fundamental principles of deception in genetic search. In: Rawlins, G.J.E. (ed.) Foundations of Genetic Algorithms, Morgan Kaufmann, San Mateo, CA (1991)

    Google Scholar 

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Authors and Affiliations

  1. Yeditepe University, Department of Computer Engineering, 34755 Kadıköy/Istanbul, Turkey

    Ender Özcan

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  1. Ender Özcan
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Edmund K. Burke Hana Rudová

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Özcan, E. (2007). Memes, Self-generation and Nurse Rostering. In: Burke, E.K., Rudová, H. (eds) Practice and Theory of Automated Timetabling VI. PATAT 2006. Lecture Notes in Computer Science, vol 3867. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77345-0_6

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  • DOI: https://doi.org/10.1007/978-3-540-77345-0_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-77344-3

  • Online ISBN: 978-3-540-77345-0

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