BACP

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.