Description

Theory
2

Laboratory
2

Instructors

Filipe Azevedo

Contents

Theoretical Program:
PT1 - Evolutionary Computation
PT2 - Genetic Algorithms
PT3 - Terminology and Operators of GA
PT4 - Classification of Genetic Algorithms
PT5 - Applications of Genetic Algorithms
PT6 - Swarm Intelligence: Introduction, Particle Swarm Optimization, Ant
Colonies Optimization

Practical Program:
PP1 - Optimization of functions using the Matlab Optimization toolbox
PP2 - Implementation of Genetic Algorithms in MATLAB
PP3 - Practical assignments

Learning Outcomes

OB1 - This course aims to give students the knowledge and skills in the area of Evolutionary Computation and Swarm Intelligence. Students should be able to interpret different types of problems and present optimization solutions using the development of computational tools that allow, through evolutionary computation, their resolution.

At the end of the course unit the student should be able to:

OB2 - Define and know the fundamental characteristics of evolutionary computation, to draw a flow chart and to explain the evolutionary algorithms, as well as to mention the advantages and applications of Evolutionary Computation.
OB3 - Understand and know how to use the fundamentals and concepts of GA;
OB4 - Know the terminology and operators of the GA in order to be able to implement GA;
OB5 - Know how to distinguish the different classes of GA;
OB6 - Address applications of the GA, namely in the area of ​​Electrical Engineering;
OB7 - Understand and know how to use the Swarm Intelligence concepts and in particular know how to implement simple problems with the Particle Swarm Optimization (PSO) algorithm.
OB8 - With the acquired knowledge and skills students should be able to have autonomy that allows them to develop their knowledge in the area of ​​Evolutionary Computation and Swarm Intelligence.