Course Computational Intelligence

Responsible: Prof. Dr. Rainer Bartz


Meets requirements of following modules(MID)

Course Organization

created 2013-06-20
valid from WS 2012/13
valid to
Course identifiers
Long name Computational Intelligence
CEID (exam identifier)

Contact hours per week (SWS)
Lecture 2
Exercise (unsplit) 1
Exercise (split)
Lab 1
Total contact hours
Lecture 30
Exercise (unsplit) 15
Exercise (split)
Lab 15
Tutorial (voluntary)
Max. capacity
Exercise (unsplit) 30
Exercise (split)
Lab 10

Total effort (hours): 150

Instruction language

  • German, 40%
  • English, 60%

Study Level

  • graduate


  • vector functions, gradient

Textbooks, Recommended Reading

  • Domschke W., Drexl A.; Einführung in Operations Research; Springer
  • Zell, A.: Simulation Neuronaler Netze; Oldenbourg
  • Nauck, D. et al.: Neuronale Netze und Fuzzy-Systeme; Vieweg
  • Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing; Springer
  • Gerdes, I. et al.: Evolutionäre Algorithmen; Vieweg
  • Grosse et al.: Taschenbuch der praktischen Regelungstechnik, Fachbuchverlag Leipzig


  • Prof. Dr. Rainer Bartz

Supporting Scientific Staff

  • tba

Transcipt Entry

Computational Intelligence


wE written exam

Total effort [hours]
wE 10

Frequency: 2/year

Course components



  • optimization strategies
    • classification of problems
    • gradient algorithms
    • simplex algorithm
    • multiobjective optimization and Pareto approach
  • artificial neural networks
    • artificial neurons
    • neural network structures
    • training algorithms
  • fuzzy logic
    • fuzzification
    • inference
    • defuzzification
  • evolutionary algorithms
    • genome representations
    • selection mechanisms
    • recombination operators
    • mutation operators

Acquired Skills
  • students acquire fundamental knowledge on theory and applications of computational intelligence
  • they know about typical classes of optimization tasks and how to map a specific problem to those classes
  • they know the simplex algorithm and can transform problems into the standard form to find the solutions
  • they can classify artificial neural networks and determine their applicability for specific tasks
  • they can vary the parameters of neural networks and rate their impact on the results
  • they can classify training algorithms and understand the backpropagation algorithm
  • they know about the fuzzy logic approach, can apply it for specific problems and justify the resulting system behavior
  • they know how evolutionary algorithms work and can distinguish the variants
  • they can transform a problem specification into a representation appropriate for an evolutionary algorithm
  • they can rate selection strategies and define suitable algorithms

Operational Competences
  • students can solve linear problems with the use of the simplex algorithm
  • they can apply artificial neural networks to solve problems of modeling and classification
  • they can define fuzzy logic systems to solve imprecise and vague tasks
  • they can solve difficult problems heuristically using evolutionary algorithms

Additional Component Assessment

fPS supervised/assisted problem solving

Contribution to course grade
fPS not rated

Frequency: 1/year



  • application of artificial neural networks to a classification task
  • variation and multiobjective optimization of neural network parameters
  • fuzzy-based closed loop control of a system with two inputs

Acquired Skills
  • students are familiar with tools supporting computational intelligence
  • they can vary system parameters, perform test series, and evaluate, present and discuss the results
  • they are able to understand, present, analyze and discuss scientific publications

Operational Competences
  • students are able to solve problems in small teams
  • they can tackle optimization tasks in a structured and systematic way
  • they can rate the behavior of a system with regard to objectives and study and improve the behavior through parameter variations
  • they are able to cope with international scientific publications, understanding, presenting and discussing them in their context

Additional Component Assessment

fSC 2 lab experiments
fLP supervised literature study and presentation

Contribution to course grade
fSC prerequisite for course exam
fLP prerequisite for course exam

Frequency: 1/year

Topic-Revision: r3 - 11 Jan 2016, GeneratedContent
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