Evolutionary Computing

2019-2020

Course Objective

This course has a threefold objective:

1) (Knowledge & Understanding) To learn about computational methods
based on Darwinian principles of evolution.
2) (Applying knowledge and understanding, Communication) To illustrate
the usage of such methods as problem solvers and as simulation tools.
3) (Applying knowledge and understanding, Lifelong learning skills) To
gain hands-on experience in performing computational experiments with
evolutionary algorithms.

Course Content

This course focuses on building, applying and studying algorithms based
on the Darwinian evolution theory. Driven by natural selection (survival
of the fittest), an evolution process is being emulated and solutions
for a given problem are being "bred". During this course all "dialects"
within evolutionary computing are treated (genetic algorithms, evolution
strategies, evolutionary programming, genetic programming). Applications
in optimisation, constraint handling, machine learning, and robotics are
discussed. Specific subjects handled include: various genetic structures
(representations), selection techniques, sexual and asexual variation
operators, (self-)adaptivity. Special attention is paid to
methodological aspects, such as algorithm design and tuning. If time
permits, subjects in Artificial Life will be handled. Hands-on-
experience is gained through a compulsory programming assignment.

Teaching Methods

Oral lectures and compulsory Java programming assignment (in teams of
3). Highly motivated students can replace the programming assignment by
a special research track under the personal supervision of the
lecturer(s). These research projects aim at publications.

Method of Assessment

Written exam and programming assignment (weighted average). To pass the
course as a whole, you must pass both the exam and the programming
assignment.

Notice that no resit is possible for the practical assignment.

Entry Requirements

Java programming skills are necessary to do the practical assignment.

Literature

Eiben, A.E., Smith, J.E., Introduction to Evolutionary Computing.
Springer, 2015, 2nd edition, ISBN 978-3-662-44873-1.

Target Audience

MSc Econometrics and Operations Research
MSc Finance
MSc Artificial Intelligence
MSc Business Analytics
MSc Computer Science
MSc Parallel and Distributed Computer Systems

General Information

Course Code X_400111
Credits 6 EC
Period P1
Course Level 400
Language of Tuition English
Faculty Faculty of Science
Course Coordinator prof. dr. A.E. Eiben
Examiner prof. dr. A.E. Eiben
Teaching Staff prof. dr. A.E. Eiben

Practical Information

You need to register for this course yourself

Last-minute registration is available for this course.

Teaching Methods Lecture
Target audiences

This course is also available as: