Evolutionary Computation

Recommended Prerequisites

Introduction to Programming and Problem solving, Introduction to Artificial Intelligence, Statistics. Readings and writing English skills.

Teaching Methods

In the lectures we will present and discuss in a critical way the theories and methods used in heuristic problem solving. Immediately after the lecture students will exercise what was taught by solving in the computer medium complexity problems. This is a group work done under the supervision of the professor. Written synthesis of a recent research work, experimental work involving the statistical study of different alternatives for a theoretical question. Work subject to oral presentation and discussion

Learning Outcomes

To present, discuss and develop natural inspired (i.e., biological, social, physical) engineering solutions to hard, complex, problems, which do not have an analytical solution or are computational intractable. To learn how to formal evaluate alternative solutions, i.e., based on sound statistical methods.
Acquiring competences in analysis and synthesis, written and oral communication (Portuguese and English), computer science and statistical knowledge, problem solving, knowledge of a foreign language, critical reasoning, group work, autonomous learning, creativity, practical application of the knowledge, research.

Work Placement(s)

No

Syllabus

1. Introduction: meta-heuristics and problem solving

2. Evolutionary Systems

2.1 - Gneral aspects

2.2- Genetic Algorithms

2.3- Genetic Programiong

2.4-  Design issues

2.5- Variants

3. Artificial Immune Systems

3.1- General aspects

3.2- Algorithms and applications

3.3- Shape Space

3.4- Nehative Selection algorithm

3.5- Clonal Selection Algorithm

3.6- Variants

4. Developmental Systems

4.1- General Aspects

4.2- Rewriting systems

4.3- Evolution and development

5. Colective Intelligence

5.1- General Aspects

5.2- Particle Swarm Algorithm

5.3- Ant Algorithm

5.4- Other Approaches

6. Parametrization and Performance.

Head Lecturer(s)

Ernesto Jorge Fernandes Costa

Assessment Methods

Assessment
Synthesis work: 10.0%
Project: 30.0%
Exam: 60.0%

Bibliography

1) Introduction to Evolutionary Computation (2nd edition), A. Eiben and J. Smith, Springer, 2015.

2) Bio-Inspired Artificial Intelligence: theories, methods, and Technologies, Dario Floreano and Claudio Mattiussi, MIT Press, 2008

3) Fundamentals of Natural Computing: basic concepts, algorithms, and applications, Leandro Castro, Chapman and Hall, 2006

4) Essentials of metaheuristics, Sean Luke, Lulu Press, 2009.

5) Manual de Computação Evolutiva e Metaheurísticas, A. Gaspar-Cunha, R. Takahashi e C.H. Antunes (Coordenadores), Imprensa da Universidade de Coimbra, 2012.

6) Clever algorithms: nature-inspired programming récipes, Jason Brownlee, ISBN 978-1-4467-8506-5, 2011.