Evolutionary and Complex Systems
1
2015-2016
03000892
Optional Specialties
Portuguese
English
Face-to-face
SEMESTRIAL
6.0
Elective
3rd Cycle Studies
Recommended Prerequisites
The master curse on Evolutionary Computation is advantageous. Good knowledge of Programming and Statistics.
Teaching Methods
We will rely on face-to-face teaching, including the presentation and discussion of the concepts and its practical applications. Students will actively participate, including by making presentations about specific topics.
Learning Outcomes
In this course we want the student to be aware of advanced topics of natural computing (Evolutionary computation and Complex Systems/Artificial Life). In the end the student must be able to be savvy about the most recent methods and techniques, be able to choose the most appropriate for a concrete problem, and be capable of validate in a rigorous way the quality of the results.
Work Placement(s)
NoSyllabus
1. Integrator Concepts
2. Principles and Environments
a. Multi-Objective Evolutionary Algorithms
b. Optimizatioon in Uncertain Environments
c. Theoretical aspects of evolutionary computation
3. Models and Architectures
a. Hiper-heuristics and self-adaptation
b. Regulation
c. Evo-Devo
4. Advanced Topics in Artificial Life
a. Multi agent systems
b. Alternatives forms of evolution (open-ended, step)
5. Applications
Head Lecturer(s)
Carlos Manuel Mira da Fonseca
Assessment Methods
Assessment
A presentation : 15.0%
A project : 35.0%
Exam: 50.0%
Bibliography
1. Baptista, T., Complexity and emergence in societies of agents, PhD thesis, Coimbra, 2012.
2. Burke, E. K., M. R. Hyde, G. Kendall, G. Ochoa, E. Ozcan and J. R. Woodward, Exploring Hyper-heuristic Methodologies with Genetic Programming, 2009. Intelligent Systems Reference Library, Springer, pp. 177-201
3. Coello, C., Lamont, G. and Veldhuizen, D., Evolutionary algorithms for solving multi-objective problems (2nd Ed.), Springer, 2007.
4. Lopes, R. and Costa, E. The regulatory network computational device, in Genetic Programming and Evolvable Machines, 2012.
5. Michaelewicz, M. and Fogel, D.B., How to solve it: modern heuristics, Springer, 2000.
6. Simões, A. – Improving memory-based evolutionary algorithms for dynamic environments. PhD thesis, Coimbra, 2010.
7. Talbi, E., Metaheuristics: from design to implementation, John Wiley and Sons, 2009