Admission requirements
No specific prerequisites required.
Description
Evolutionary Computation is a field of computer science dealing with algorithms gleaned from the model of organic evolution – so-called evolutionary algorithms. The idea is to let the computer evolve solutions to problems rather than trying to “calculate” them.
Evolutionary algorithms do this by using the fundamental principles of evolution such as, for example, selection, mutation and recombination among a population of simulated individuals. The evolutionary approach is used today in a variety of application areas for solving problems that require intelligent behaviour, adaptive learning and optimization. These fields include e.g. engineering optimization, artificial life, automatic programming, autonomous agents, and evolutionary economics.
Due to the large diversity of the field, the course focuses on the fundamentals of biological evolution as the underlying motivation, the main variants of evolutionary algorithms (genetic algorithms and evolution strategies), application examples, and some outlook into related aspects of evolutionary computation.
Course objectives
The course gives a comprehensive of the field through a series of lectures and exercises. In addition, a practical application exercise of evolutionary computation is given to the students, who are expected to run experiments and write a short report about the experiment and the results obtained. This report will be written in scientific paper format, and we will encourage and help the authors of the best student paper, provided that the quality of the results is sufficient, to submit this paper to a scientific conference.
Learn the main algorithms in the field, in particular genetic algorithms and evolutionary strategies.
Understand the underlying principles of evolutionary computation.
Learn the theoretical foundations of evolutionary computation.
Apply the algorithms to some application area, and obtain some practical experience.
Learn about applications in science and industry.
Learn how to write a short scientific paper in evolutionary computation.
Timetable
The most recent timetable can be found at the LIACS website
Mode of instruction
Lectures, mandatory werkcollege
Assessment method
The final grade is a combination of grades for
(1) the written exam (60%) and
(2) the report about the practical assignment (40%).
Reading list
The following books are recommended but not mandatory for the course:
1) Th. Bäck: Evolutionary Algorithms in Theory and Practice, Oxford, University Press, NY, 1996. ISBN-13: 978-0195099713
2) A.E. Eiben, J.E. Smith: Introduction to Evolutionary Computing, Springer, Berlin, 2008. ISBN-13: 978-3540401841Slides will be provided to the students for download.
Registration
You have to sign up for classes and examinations (including resits) in uSis. Check this link for more information and activity codes.
Contact information
Study coordinator Computer Science, Riet Derogee