|
Informations Générales
|
|
Intitulé du cours *
|
Heuristic optimisation
|
|
Langue d'enseignement *
|
Enseigné en anglais
|
|
Cycle *
|
Cycle 2
|
|
Niveau dans le cycle *
|
Niveau 1 dans le cycle
|
|
Discipline *
|
Sciences informatiques
|
|
Titulaire(s) *
|
Thomas,T STUTZLE
|
|
Pré-requis
|
|
Cours pré-requis
|
|
|
Autres pré-requis
|
Computer science basics, programming experience in at least one procedural or object oriented language.
|
|
Place du cours dans le programme
|
|
|
Objectifs et méthodologies
|
|
Objectifs du cours et compétences visées *
|
The main objective is to give students theoretical and practical knowledge of how to tackle effectively difficult optimization problems with heuristic techniques. In more detail, the goals are
* Learn about heuristic optimization techniques
* Learn how these can be used to tackle optimization problems
* Learn how to analyze heuristic algorithms empirically.
* Obtain hands-on experience with the implementation and the application of heuristic techniques.
|
|
Contenu du cours *
|
Computationally hard problems arise in many relevant application areas of computational intelligence such as computer science, operations research, bioinformatics, and engineering. For many such problems, heuristic search techniques are the most successful methods.
This course introduces and discusses heuristic optimization techniques with a main focus on stochastic local search techniques. The course illustrates the application principles of these algorithms using a number of example applications. A significant focus in the course will be also on relevant techniques for the empirical evaluation of heuristic optimization algorithms and issues that arise in their design and development. Hands-on experience with these algorithmic techniques will be gained in accompanying practical exercises.
|
|
Méthodes d'enseignement *
|
The course consists of lectures, exercise sessions, where students deepen some topics covered in the lectures, and implementation tasks. The course is taught in English.
|
|
Syllabus *
|
|
|
Autres supports de cours
|
|
|
Références, bibliographie et lectures recommandées *
|
The course is mainly based on the book
* Holger Hoos and Thomas Stuetzle. Stochastic Local Search-Foundations and Applications, Morgan Kaufmann Publishers, San Francisco, California, 2004.
Other relevant literature for the lecture is:
* Emile H. L. Aarts und Jan Karel Lenstra (editors), Local Search in Combinatorial Optimization. John Wiley and Sons, 1997.
* Marco Dorigo und Thomas Stuetzle, Ant Colony Optimization. MIT Press, 2004.
* Zbigniew Michalewicz and David Fogel, How to Solve it: Modern Heuristics. Springer Verlag, 2000.
|
|
Evaluation
|
|
Méthode *
|
Oral examination
|
|
Construction de la note, pondération des différentes activités *
|
|
|
Priorités de l'enseignant
|
|
|
Conseils spécifiques
|
|
|
Langue d'évaluation *
|
|
|
Organisation pratique
|
|
Institution organisatrice *
|
ULB
|
|
Faculté gestionnaire *
|
Ecole polytechnique Bruxelles
|
|
Horaire *
|
Premier quadrimestre -
Deuxième quadrimestre
|
|
Coordination pédagogique
|
|
|
Contact *
|
|
|
Lieu d’enseignement *
|
|
|
Remarques
|
|
|
|
|
