Hybridization of genetic algorithms and constraint propagation for the BACP Constraint Satisfaction Problems (CSP) provide a modelling framework for many computer aided decision making problems. Many of these problems are associated to an optimization criterion. Solving a CSP consists in finding an assignment of values to the variables that satisfies the constraints and optimizes a given objective function (in case of an optimization problem). In this paper, we extend our framework for genetic algorithms (GA) as suggested by the reviewers of our previous ICLP paper [E. Monfroy, F. Saubion and T. Lambert, “On hybridization of local search and constraint propagation”, Lect. Notes Comput. Sci. 3132, 299–313 (2004; Zbl 1104.68722)]. Our purpose is not to solve efficiently the Balanced Academic Curriculum Problem (BACP) [C. Castro and S. Manzano, “Variable and value ordering when solving balanced academic curriculum problems”, in: Proc. 6th Workshop of the ERCIM WG on Constraints, 12 p. (2001), available at http://arxiv.org/abs/cs/0110007v1] but to combine a genetic algorithm with constraint programming techniques and to propose a general modelling framework to precisely design such hybrid resolution process and highlight their characteristics and properties.
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References in zbMATH (referenced in 4 articles , 1 standard article )
Showing results 1 to 4 of 4.
- Ceschia, Sara; Di Gaspero, Luca; Schaerf, Andrea: The generalized balanced academic curriculum problem with heterogeneous classes (2014)
- Ünal, Yusuf Ziya; Uysal, Özgür: A new mixed integer programming model for curriculum balancing: application to a Turkish university (2014)
- Rubio, José-Miguel; Palma, Wenceslao; Rodriguez, Nibaldo; Soto, Ricardo; Crawford, Broderick; Paredes, Fernando; Cabrera, Guillermo: Solving the balanced academic curriculum problem using the ACO metaheuristic (2013)
- Lambert, Tony; Castro, Carlos; Monfroy, Eric; Riff, María Cristina; Saubion, Frédéric: Hybridization of genetic algorithms and constraint propagation for the BACP (2005)