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Modelling, Simulation and Optimisation

This module deals with selected methods out of the research and application area of Computational Intelligence. Methods treated in the lectures and seminar projects are, for instance, evolutionary search and optimisation technologies, neural networks, sophisticated data mining technologies, artificial intelligence, and every kind of hybrid intelligent system.

Besides the basic foundations and a broader theory, these methods are applied to business issues or other application areas of interest for modelling, simulating and analysing problems, for evaluating and assessing data, as well as for obtaining viable alternatives and optimised solutions.

The potential impact of computational intelligence is investigated. Different cases are examined where computational intelligence can provide a substantial support, for instance in management science,operations research, logistics, finance and banking, and computer science.

On successful completion of this module, the students will have gained knowledge of the objectives, implementation and use of such methods of computational intelligence.

ECTS
Type
Level

6
Core Course
Advanced

Should be elected: FT: 3rd
PT: 3rd or 5th
Academic Module Coordinator Prof. Dr. Rolf Dornberger
Lecturers Prof. Dr. Thomas Hanne
Pre-requisites

Stuctural Sciences: Quantitative Methods for Business (foundations in mathematics, statistics, and optimization), basic knowledge in programming (preferably Java)

Overall hours Contact hours: 60 h
Self-Study: 120 h
Outline Content
  • Overview of optimization problems
    • Defining, assessing and solving optimization problems
    • Objectives, constraints, parameter sets
  • Application / business areas
    • Examples where computational intelligence is supporting business areas
    • Logistics (airline, railway, etc.), engineering, finance, economics, management
  • Overview of computational intelligence
    • Evolutionary computation (focus), artificial neural networks, fuzzy logic
  • Optimization methods and metaheuristics
    • Genetic algorithm, evolution strategy, simulated annealing, swarm intelligence, ant colony based optimization
  • Software platform for optimization and machine learning
    • Using and extending the software platform
    • Repetition of programming and software engineering: Syntax and usage of Java, object-oriented programming
Teaching and Learning Methods This module is taught in plenary lectures and plenary/group workshops, including presentations and practical training with software tools. Class participation is actively encouraged.

Plenary sessions will be accompanied by assignments to be done as homework. In the plenary lectures, students may be required to orally present results of guided self study or repetition of previous lectures.
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