Solving the balanced academic curriculum problem with an hybridization of genetic algorithm and constraint propagation

  • Authors:
  • T. Lambert;C. Castro;E. Monfroy;F. Saubion

  • Affiliations:
  • LINA, Université de Nantes, France;Universidad Santa María, Valparaíso, Chile;LINA, Université de Nantes, France;LERIA, Université d'Angers, France

  • Venue:
  • ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
  • Year:
  • 2006

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Abstract

In this paper, we are concerned with the design of a hybrid resolution framework including genetic algorithms and constraint propagation to solve the balanced academic curriculum problem. We develop a theoretical model in which hybrid resolution can be achieved as the computation of a fixpoint of elementary functions. These functions correspond to basic resolution techniques and their applications can easily be parameterized by different search strategies. This framework is used to solve a specific problem and we discuss the experimental results showing the interest of the of the model to design such hybridizations.