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Genetic Algorithms Used to Solve Scheduling Problems

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Abstract

A general description of genetic algorithms is given. An optimal scheduling problem is examined from the viewpoint of real needs of a modern university. A solution, based on a genetic algorithm, is proposed for the problem. The genetic algorithm implementations are reviewed and their convergence rate and quality are analyzed.

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REFERENCES

  1. H. Muhlenbein and D. Schlierkamp-Voosen, “Predictive models for the breeder genetic algorithm,” Evolutionary Computation, 1, No. 1, 25-50 (1993).

    Google Scholar 

  2. J. J. Grefensette and J. E. Baker, “How genetic algorithms work: A critical look at implicit parallelism,” in: Proc. 3rd Intern. Conf. on Genetic Algorithms, Morgan Kaufmann, San Mateo (1989), pp. 20-27.

    Google Scholar 

  3. A. E. Eiben, P.-E. Raue, and Z. Ruttkay, “Genetic algorithms with multi-parent recombination,” in: Proc. 3rd Conf. on Parallel Problem Solving from Nature, Springer-Verlag, Berlin (1994), pp. 78-87.

    Google Scholar 

  4. G. Syswerda, “Uniform crossover in genetic algorithms,” in: Proc. 3rd Intern. Conf. on Genetic Algorithms, Morgan Kaufmann, San Mateo (1989), pp. 2-9.

    Google Scholar 

  5. N. J. Radcliffe, “Equivalence class analysis of genetic algorithms,” Complex Systems, 5, No. 2, 183-205 (1990).

    Google Scholar 

  6. Yu. V. Kapitonova and A. A. Letichevskii, “Theorem proving in a mathematical information environment,” Kibern. Sist. Analiz, No. 4, 3-12 (1998).

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

    Google Scholar 

  8. R. Poli, “Exact schema theory for genetic programming and variable-length genetic algorithms with one-point crossover,” in: Genetic Programming and Evolvable Machines, Morgan Kaufmann, Las Vegas (2001), pp. 469 476.

    Google Scholar 

  9. T. Blickle and L. Thiele, “A mathematical analysis of tournament selection,” in: Proc. 6th Intern. Conf. on Genetic Algorithms (ICGA' 95), Morgan Kaufmann, San Mateo (1995), pp. 9-16.

    Google Scholar 

  10. D. Whitley, “The GENITOR algorithm and selection pressure,” in: J. D. Schaffer (ed.), Proc. 3rd Intern. Conf. on Genetic Algorithms, Morgan Kaufmann, San Mateo (1989), pp. 116-121.

    Google Scholar 

  11. J. Hesser and R. Manner, “Towards an optimal mutation probability for genetic algorithms,” Parallel Problem Solving from Nature, 11, 96-119 (1991).

    Google Scholar 

  12. T. Haynes, R. Wainwright, S. Sen, and D. Schoenefeld, “Strongly typed genetic programming in evolving cooperation strategies,” in: Proc. 6th Intern. Conf. on Genetic Algorithms, Morgan Kaufmann, San Mateo (1995), pp. 271-278.

    Google Scholar 

  13. T. Jansen and I. Wegener, “On the choice of the mutation probability for the (1 + 1) EA,” Parallel Problem Solving from Nature, 6, 233-239 (2000).

    Google Scholar 

  14. T.C. Fogarty, “Varying the probability of mutation in the genetic algorithm,” in: Proc. 3rd Intern. Conf. on Genetic Algorithms, Morgan Kaufmann, La Jolla, CA (1989), pp. 104-109.

    Google Scholar 

  15. R. Poli and W. B. Langdon, “Genetic programming with one-point crossover,” in: Proc. 2nd On-line World Conf. on Soft Computing in Engineering Design and Manufacturing, Springer-Verlag, London (1997).

    Google Scholar 

  16. T. Blickle and L. Thiel, “Genetic programming and redundancy,” in: Genetic Algorithms within the Framework of Evolutionary Computation, Max-Plank-Institut für Informatik, Saarbrucken (1994), pp. 33-38.

    Google Scholar 

  17. S. Evan, A. Itai, and A. Shamir, “On the complexity of timetable and multicom-modify flow problems,” SIAM J. Comp., 5, No. 4, 691-703 (1976).

    Google Scholar 

  18. W. Erben and K. Keppler, “A general algorithm for solving a weekly course timetabling problem”, in: Lecture Notes in Computer Science, 1153, Springer-Verlag, London (1999), pp. 198-211.

    Google Scholar 

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Glibovets, N.N., Medvid', S.A. Genetic Algorithms Used to Solve Scheduling Problems. Cybernetics and Systems Analysis 39, 81–90 (2003). https://doi.org/10.1023/A:1023825226796

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