Evolutionary computing (2024/2025: Periode 3)

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Cursusdoel

After completing the course, students have
  • a thorough knowledge of the concepts, techniques, analyses, and algorithms in the field of evolutionary computation and meta-heuristic search algorithms.
  • theoretical knowledge to understand the behavior of evolutionary and meta-heuristic search algorithms.
  • a thorough knowledge of state-of-the-art applications of evolutionary computation and meta-heuristic search algorithms.
  • a thorough knowledge of solving multi-objective optimization problems with metaheuristic search algorithms.
and are capable of
  • designing efficient and high performance meta-heuristic search problem for diverse discrete optimization problems.
  • reading and understanding key journal publications in the field of evolutionary computation and meta-heuristic search algorithms.
  • experimentally comparing different meta-heuristic search algorithms on a set of benchmark problems.
  • implementing meta-heuristic search algorithms to solve hard, discrete optimization problems.
  • analyzing the performance and sensitivity of meta-heuristic search algorithms.
  • performing a statistically sound analysis of the experimental results of different meta-heuristic search algorithms.
  • working together with other students on designing, building, and testing evolutionary and meta-heuristic search algorithms.
  • making English language presentations in writing of one’s own research.
  • making English language presentations orally of one’s own research.
Assessment
The assessment consists of
  • a written exam (50% of the final mark)
  • lab assignments (40%)
  • paper report (10%).
To qualify for a repair of the final result the mark needs to be at least a 4, or “AANV”.

Vakinhoudelijk

Evolutionary algorithms are population-based, stochastic search algorithms based on the mechanisms of natural evolution.
This course covers how to design representations and variation operators for specific problems.
Furthermore convergence behavior and population sizing are analyzed.
The course focuses on the combination of evolutionary algorithms with local search heuristics to solve combinatorial optimization problems like graph bi-partitioning, graph coloring, and bin packing.

Course form
Lectures, lab sessions.

Study material
To be announced.

Werkvormen

Hoorcollege

Toetsing

Eindresultaat

Verplicht | Weging 100% | ECTS 7,5

Ingangseisen en voorkennis

Ingangseisen

Je moet een geldige toelatingsbeschikking hebben

Voorkennis

Er is geen informatie over benodigde voorkennis bekend.

Voertalen

  • Engels

Cursusmomenten

Gerelateerde studies

Tentamens

Er is geen tentamenrooster beschikbaar voor deze cursus

Verplicht materiaal

Er is geen informatie over de verplichte literatuur bekend


Aanbevolen materiaal

Er is geen informatie over de aanbevolen literatuur bekend

Coördinator

dr. ir. D. Thierens D.Thierens@uu.nl

Docenten

dr. ir. D. Thierens D.Thierens@uu.nl

Inschrijving

Deze cursus is open voor bijvakkers. Controleer wel of er aanvullende ingangseisen gelden.

Inschrijving
Van maandag 4 november 2024 tot en met vrijdag 22 november 2024

Na-inschrijving
Van maandag 20 januari 2025 tot en met maandag 17 februari 2025

Inschrijving niet geopend
Permanente link naar de cursuspagina
Laat in de Cursus-Catalogus zien
Laat in MyTimetable zien