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Nature-inspired optimization methods SS 2022

Author

Pradyumn Shukla

Participating institute

Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB)

Genre

Vorlesung

Description

Lecture

Subject area

Economics

License

KITopen Licence

Note

You can subscribe to the contributions of this series as a podcast.

Embed Code

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Episodes 1-14 of 14

Nature-inspired optimization methods SS 2022 : Modelling fairness in resource allocation problems

Nature-inspired optimization methods SS 2022 : Robust Design Optimization

Nature-inspired optimization methods SS 2022 : Chapter: Constraint handling techniques

Nature-inspired optimization methods SS 2022 : User preferences, distributed computing

Nature-inspired optimization methods SS 2022 : Chapter: An Algorithmic Framework for Many-Objective-Optimization

Nature-inspired optimization methods SS 2022 : Multi-objective Evolutionary Algorithms

Nature-inspired optimization methods SS 2022 : Search operators

Nature-inspired optimization methods SS 2022 : Chapter: Evolutionary algorithms

Nature-inspired optimization methods SS 2022 : Chapter: Representation

Nature-inspired optimization methods SS 2022 : Chapter: Escaping Local Minima II. Tabu Search

Nature-inspired optimization methods SS 2022 : Chapter: Escaping Local Minima

Nature-inspired optimization methods SS 2022 : Problem Solving and Basic Concepts II. Basic Concepts

Nature-inspired optimization methods SS 2022 : Chapter 1: Problem Solving and Basic Concepts

Nature-inspired optimization methods SS 2022 : Introductory lecture