inf535 - Computational Intelligence I

inf535 - Computational Intelligence I

Department of Computing Science 6 KP
module responsibility
  • Oliver Kramer
Prüfungsberechtigt
  • Die im Modul Lehrenden
Module components Semester courses Wintersemester 2025/2026 Examination
Lecture
  • Unlimited access 2.01.535 - Evolution Strategies Show lecturers
    • Prof. Dr. Oliver Kramer
    • Jill Baumann

    The course times are not decided yet.
    The “Evolution Strategies” lecture explores key optimization techniques for solving complex problems. It introduces core concepts and covers algorithms like the (1+1)-ES and the (μ+λ)-ES, including mechanisms such as the 1/5 success rule, restarts, self-adaptation, and covariance matrix estimation. Emphasis is placed on understanding and programming these methods in Python through hands-on exercises. Experimental results and benchmark functions illustrate their effectiveness. The course is worth 6 ECTS.
Exercises
Hinweise zum Modul
Prerequisites

Basics of statistics
Prüfungszeiten

At the end of the lecture period
Module examination

Written or oral exam
Skills to be acquired in this module

After successful completion of the course, students should have acquired the ability to master the presented methods in theory and practice. The students should be able to recognize and model corresponding optimization and data analysis problems themselves and to apply the methods unerringly.

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

Self-competence
The students:

  • evaluate own skills with reference to peers
  • realize personal limitations
  • adapt own problem solving approaches with reference to required method competences

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