Evolutionary Computing

Code	14492
Year	1
Semester	S1
ECTS Credits	6
Workload	OT(15H)
Scientific area	Informatics
Entry requirements	None.
Mode of delivery	Face-to-face
Learning outcomes	Evolutionary computation provides approximate solutions to various scientific and engineering problems in polynomial time. This course offers a broad introduction to the field of Evolutionary Computation leading to the exploration of recent search, optimization and learning techniques. Students should be able to: - formulate problems in evolutionary computation. - assess the strengths and weaknesses of several approaches to evolutionary computation. - assess and understand the key commonalities and differences in various evolutionary computation models. - apply techniques in evolutionary computation to problems such as optimization, automatic programming, control, and biological modeling.
Syllabus	1. Genetic Algorithms 2. Genetic Programming 3. Evolution Strategies 4. Evolutionary Programming 5. Hybrid optimization techniques 6. Research proposal
Main Bibliography	Required reading: The main references are selected from a set of papers about current research (foundational and applicational) on the field of Evolutionary Computing. Introduction to Evolutionary Computing, Eiben and Smith. Springer-Verlag, New York, 2003. Evolutionary computation: a unified approach, Kenneth A. De Jong, MIT Press, 2006. Scientific papers proposed by the teacher. Recommended reading: Evolutionary Computation for Modeling and Optimization, Daniel Ashlock, Springer Verlag, 2006. Handbook of Evolutionary Computation, Bäck, T., Fogel, D., Michalewicz, Z., Oxford Univ. Press, 1997.
Language	Portuguese. Tutorial support is available in English.