|OFICIAL||Electrical and Computer Engineering|
|Responsible unit:||Department of Industrial Engineering and Management|
|Course/CS Responsible:||Doctoral Program in Electrical and Computer Engineering|
|Acronym||No. of Students||Study Plan||Curricular Years||Credits UCN||Credits ECTS||Contact hours||Total Time|
|PDEEC||11||Syllabus since 2015/16||1||-||7,5||70||202,5|
|Bernardo Sobrinho Simões de Almada Lobo|
To give the first-year PhD students a broad, but simultaneously in-depth, overview of search and optimization methodologies, applicable to the resolution of multi-disciplinary decision problems, under a decision support framework.
It is expected to endow the students with skills to:
- identify optimization problems and approach them in a structured way;
- define the most adequate abstraction level to model optimization problems for an algorithmic approach to their resolution.
- identify the algorithmic techniques to solve a particular optimization problem;
- use heuristics and metaheuristics methods to obtain solutions for the problems;
- implement, test and validate, search methodologies to solve different classes of optimization problems.
Heuristics and Local Search
Divide and Conquer and Dynamic Programming
Branch and Bound and A* Algorithms
Dealing with infeasibility in search methods
- Simulated Annealing
- Tabu Search
- Variable Neighbourhood Search
- Other non-populational metaheuristics
- Genetic algorithms and evolutionary programming
- Ant Colonies
- Other populational metaheuristics
Classes will be mainly organized as lectures, in opposition to home work that will mainly be organized around assignments and therefore will take place in off class periods.
A strong interaction and participation of students, leading to a real active learning environment, will be sought in the lectures. In concrete the following learning strategies will be used:
- Group discussion based on scientific papers
- Small problems discussion and resolution
- Exploitation of alternative problem development paths
- Resolution of an optimization project
|Frequência das aulas||42,00|
The components for student evaluation are:
- Individual work assignment on the preparation of a scientific paper that reports the implementation of a neighborhood-based search to a standard combinatorial optimization problem (weight of 50%)
- Group work (weight of 50%), about the implementation of a population or neighborhood-based search heuristc to address an optimization problem within the scope of the PhD project of the student.
Each component will be graded and the final score will be calculated as the weighted average of all components.
These students will be subject to all evaluation procedures of regular students, i.e., they must deliver their assignments specified during the course plus any special works also specified, being the only difference towards regular students that they are not required to attend classes and deliver assignments in the same dates as regular students, in the cases the law specifically states it.