Solving real-life Time-Tabling Problems
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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