Solving real-life Time-Tabling Problems

  • Martin Schmidt
Communications 8B Evolutionary Computation
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1609)


We will compare the performance of a Genetic Algorithm and Simulated Annealing on instances of difficult real-life Time-Tabling Problems. We will further explain how the performance can be increased significantly by applying a general and very effective genotype-to-phenotype decoding. Furthermore, we will contrast the performance of Lamarckian Learning to the performance of Baldwin Learning. Finally, we test and comment on the benefits of adaptive operators for both Genetic Algorithms and Simulated Annealing.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Martin Schmidt
    • 1
  1. 1.Dept. of Computer ScienceUniversity of AarhusDenmark

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