| List |
| Topic: | Automatické generování heuristických optimalizačních algoritmů |
|---|---|
| Department: | Katedra kybernetiky |
| Supervisor: | IMR |
| Announce as: | Diplomová práce, Bakalářská práce, Semestrální projekt |
| Description: | Metaheuristic algorithms are highly scalable methods, that are widely used for obtaining good-quality solutions to large instances of NP-hard optimization problems, otherwise intractable by exact solvers. However, these algorithms are typically custom-built and problem-specific. Their design is time-consuming and requires expertise and experience.
During this project, you will work with our generic metaheuristic solver, which is capable of solving a large class of combinatorial optimization problems with permutative solution representation. The solver consists of numerous alternative building blocks (metaheuristics, heuristics, local search operators, etc.). Given a specific problem and a training set of instances, the goal is to automatically generate a well-performing problem-specific algorithm. Contact: wolledav@cvut.cz http://imr.ciirc.cvut.cz/People/David IMR CIIRC ČVUT |
| Bibliography: | Thomas Stützle, Manuel López-Ibáñez; Automated Design of Metaheuristic Algorithms, Handbook of Metaheuristics, Springer 2019 |