Stud.IP Uni Oldenburg
University of Oldenburg
22.01.2022 14:00:02
inf535 - Computational Intelligence I (Complete module description)
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Module label Computational Intelligence I
Module code inf535
Credit points 6.0 KP
Workload 180 h
Institute directory Department of Computing Science
Applicability of the module
  • Master Applied Economics and Data Science (Master) > Data Science
  • Master's Programme Business Informatics (Master) > Akzentsetzungsmodule der Informatik
  • Master's Programme Computing Science (Master) > Angewandte Informatik
  • Master's Programme Engineering of Socio-Technical Systems (Master) > Embedded Brain Computer Interaction
  • Master's Programme Engineering of Socio-Technical Systems (Master) > Human-Computer Interaction
  • Master's Programme Environmental Modelling (Master) > Mastermodule
Responsible persons
Kramer, Oliver (Authorized examiners)
Lehrenden, Die im Modul (Authorized examiners)
Skills to be acquired in this module
Professional competence:
The students:
  • recognise optimisation problems
  • implement simple algorithms of heuristic optimisation
  • critically discuss solutions and selection of methods
  • deepen previous knowledge of analysis and linear algebra

Methodological competence
The students:
  • deepen programming skills
  • apply modelling skills
  • learn about the relation between problem class and method selection

Social competence
The students:
  • cooperatively implement content introduced in lecture
  • evaluate own solutions and compare them with those of their peers

The students:
  • evaluate own skills with reference to peers
  • realize personal limitations
  • adapt own problem solving approaches with reference to required method competences
Module contents
Computational Intelligence comprises intelligent and adaptive methods for optimisation and learning. The module "Computational Intelligence I" concentrates on methods for evolutionary optimisation and heuristic approaches. The exercises introduce and deepen practical aspects of the implementation and algorithmic design, also taking into account application aspects.

Overview of Content:
  • foundations of optimisation
  • genetic algorithms and evolution strategies
  • parameter control and self-adaptation
  • runtime analysis
  • swarm algorithms
  • constrained optimisation
  • multi-objective optimisation
  • meta-modeling
Reader's advisory
  • EIBEN, A. E.; SMITH, J. E.: Introduction to Evolutionary Computing. Springer, 2003.
  • KENNEDY, J.; EBERHART, R.C.; YUHUI, S.: Swarm Intelligence. Morgan Kaufmann, 2001.
  • KRAMER, O.: Computational Intelligence. Springer, 2009.
  • RUTKOWSKI, L.: Computational Intelligence - Methods and Techniques. Springer, 2008.
  • ROJAS, R.: Theorie der neuronalen Netze: Eine systematische Einführung. Springer, 1993.
Languages of instruction English , German
Duration (semesters) 1 Semester
Module frequency jährlich
Module capacity unlimited
Modullevel / module level AS (Akzentsetzung / Accentuation)
Modulart / typ of module je nach Studiengang Pflicht oder Wahlpflicht
Lehr-/Lernform / Teaching/Learning method
Vorkenntnisse / Previous knowledge - Grundlagen der Statistik
Course type Comment SWS Frequency Workload of compulsory attendance
2 WiSe 28
2 WiSe 28
Total time of attendance for the module 56 h
Examination Time of examination Type of examination
Final exam of module
At the end of the lecture period
Written or oral exam