Checking date: 15/07/2021

Course: 2021/2022

Methods and optimization techniques
Study: Master in Industrial Mechanical (265)

Coordinating teacher: BARBA NIETO, ALVARO

Department assigned to the subject: Department of Mechanical Engineering

Type: Electives
ECTS Credits: 3.0 ECTS


Requirements (Subjects that are assumed to be known)
It is recommended to be engineer in industrial and production field
COMPETENCES - Knowledge on local and global optimization methods. - Knowlesdge on parametric and nonparametric techniques for predicting. LEARNING RESULTS - Ability to identify and resolve real problems.
Skills and learning outcomes
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
Assessment System
  • % end-of-term-examination 40
  • % of continuous assessment (assigments, laboratory, practicals...) 60
Calendar of Continuous assessment
Basic Bibliography
  • 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.