Computational Intelligence for Optimization
Objectives
This course should introduce students to the basic concept of optimization and to a set of heuristic methods for solving, or approxumating, optimization problems. At the same time, this discipline should help students acquiring some bases of programming.
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
(Black)board and slides for theoretical classes, projection of a programming environment for software development in the practical classes.
Evaluation method
First epoch: weighted average between the average grade obtained in a set of evaluations along the semester and the final test.
Second epoch: final test.
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