Name
Abstract | ||
|---|---|---|
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 [5]. Our purpose is not to solve efficiently the Balanced Academic Curriculum Problem (BACP) [2] 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. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11562931_38 | ICLP |
Keywords | DocType | Volume |
balanced academic curriculum problem,general modelling framework,hybrid resolution process,previous iclp paper,genetic algorithm,constraint propagation,constraint satisfaction problems,optimization criterion,optimization problem,constraint programming technique,modelling framework,objective function,constraint programming,satisfiability,constraint satisfaction problem | Conference | 3668 |
ISSN | ISBN | Citations |
0302-9743 | 3-540-29208-X | 1 |
PageRank | References | Authors |
0.51 | 4 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tony Lambert | 1 | 20 | 2.72 |
| Carlos Castro | 2 | 255 | 29.05 |
| Eric Monfroy | 3 | 579 | 63.05 |
| María Cristina Riff | 4 | 200 | 23.91 |
| Frédéric Saubion | 5 | 312 | 37.00 |