Computational Intelligence for Optimization
Objectives
Introducing the basics of optimization and the main Computational Intelligence algorithms and techniques for solving complex optimization problems.
General characterization
Code
200142
Credits
7.5
Responsible teacher
Leonardo Vanneschi
Hours
Weekly - Available soon
Total - Available soon
Teaching language
Portuguese. If there are Erasmus students, classes will be taught in English
Prerequisites
No requirement
Bibliography
“Simulated Annealing and Boltzmann Machines”, E. Aarts and J. Korst, John Wiley and Sons; “Genetic Algorithms in Search, Optimization and Machine Learning”, D. E. Goldberg, Addison-Wesley; 0; 0; 0
Teaching method
(White)board and slides for theoretical classes, projection of a programming environment for software development in the practical classes.
Evaluation method
Both First and Second examination epoch: weighted average between a final exam and a project.
Subject matter
- Motivations of the course
- Optimization Problems
- Fitness Landscapes
- Hill Climbing
- Simulated Annealing
- Hints to Tabu Search
- Genetic Algorithms
- Advanced Genetic Algorithms methods
- Hints to Particle Swarm Optimization
- Genetic programming