Course ObjectiveThis course has a threefold objective: 1) To learn about computational
methods based on Darwinian principles of evolution. 2) To illustrate the
usage of such methods as problem solvers and as simulation tools. 3) To
gain hands-on experience in performing computational experiments with
Course ContentThe course is treating various 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
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 by a compulsory programming
Teaching MethodsOral 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 AssessmentWritten exam and programming assignment (weighted average). To pass the
course as a whole, you must pass both the exam and the programming
Entry RequirementsJava programming skills are necessary to do the practical assignment.
LiteratureEiben, A.E., Smith, J.E., Introduction to Evolutionary Computing.
Springer, 2015, 2nd edition, ISBN 978-3-662-44873-1.
Target AudiencemBA, mAI, mCS, mPDCS
|Language of Tuition||English|
|Faculty||Faculty of Science|
|Course Coordinator||prof. dr. A.E. Eiben|
|Examiner||J.V. Heinerman MSc|
J.V. Heinerman MSc
prof. dr. A.E. Eiben
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