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.
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Lambert, T., Castro, C., Monfroy, E., Saubion, F. (2006). Solving the Balanced Academic Curriculum Problem with an Hybridization of Genetic Algorithm and Constraint Propagation. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2006. ICAISC 2006. Lecture Notes in Computer Science(), vol 4029. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11785231_44
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DOI: https://doi.org/10.1007/11785231_44
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