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teaching:infoh413 [2010/02/10 16:14] ezimanyi |
teaching:infoh413 [2010/03/20 12:19] boverhae |
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| ====== INFO-H-413 : Heuristic optimization ====== | ====== INFO-H-413 : Heuristic optimization ====== | ||
| - | <note tip> | + | |
| - | [[http://cs.ulb.ac.be/public/teaching/infoh414#support_de_cours|Support de cours]]\\ | + | |
| - | [[http://cs.ulb.ac.be/public/teaching/infoh414#horaire_des_travaux_pratiques|Horaire]]\\ | + | |
| - | </note> | + | |
| ===== Lecturers ===== | ===== Lecturers ===== | ||
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| 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 have been established as the most successful methods. In this course I will introduce and discuss heuristic optimization techniques with a main focus on stochastic local search techniques, which are the most relevant heuristic techniques. The course will illustrate the application principles of these algorithms using a number of example applications ranging from rather simple problems of more academic interest to more complex problems from real applications. A significant focus in the course will be also on relevant techniques for the empirical evaluation of heuristic optimization algorithms and the issues that arise in their development. Hands-on experience with these algorithmic techniques will be gained in accompanying practical exercises. | 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 have been established as the most successful methods. In this course I will introduce and discuss heuristic optimization techniques with a main focus on stochastic local search techniques, which are the most relevant heuristic techniques. The course will illustrate the application principles of these algorithms using a number of example applications ranging from rather simple problems of more academic interest to more complex problems from real applications. A significant focus in the course will be also on relevant techniques for the empirical evaluation of heuristic optimization algorithms and the issues that arise in their development. Hands-on experience with these algorithmic techniques will be gained in accompanying practical exercises. | ||
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| ===== Bibliography ===== | ===== Bibliography ===== | ||
| - | * Dorigo M. & T. Stützle (2004). Ant Colony Optimization. Cambridge, MA: MIT Press/Bradford Books | + | The course is mainly based on the book |
| - | * Bonabeau E., M. Dorigo & G. Theraulaz (1999). Swarm Intelligence: From Natural to Artificial Systems. New York, NY: Oxford University Press | + | * 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. | ||
| ===== Teaching methods ===== | ===== Teaching methods ===== | ||
| - | Ex catedra and projects, course taught in English | + | The course consists of lectures, exercise sessions, where students deepen some topics covered in the lectures, and implementation tasks, course taught in English |
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| + | ===== Assessment ===== | ||
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| + | Oral examination | ||
| + | ===== ECTS ===== | ||
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| + | total: 4 ECTS (theory: 2, exercices: 2) | ||
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