Department assigned to the subject: Department of Mechanical Engineering
ECTS Credits: 3.0 ECTS
Students are expected to have completed
It is recommended to be engineer in industrial and production field
Competences and skills that will be acquired and learning results.
- Knowledge on local and global optimization methods.
- Knowlesdge on parametric and nonparametric techniques for predicting.
- Ability to identify and resolve real problems.
Description of contents: programme
1. Introduction to optimization in mechanical engineering
2. Local optimization methods
3. Global optimization methods. Genetic Algorithms
4. Neural Networks
Learning activities and methodology
- Theory session
- Theory sessions: to solve exercises and cases
- Practical cases and exercises
- Final exam
% end-of-term-examination 40
% of continuous assessment (assigments, laboratory, practicals...) 60
Arora. Introduction to optimum design. Elsevier.
Goldberg, D.. Genetic algorithms in search, optimization and machine learning. Addison-Wesley.
Haykin,S.. Neural Networks. A comprehensive foundation. Prentice Hall.
The course syllabus and the academic weekly planning may change due academic events or other reasons.