Data Science and Artificial Intelligence

Swarm intelligence and evolutionary algorithms

Integrated course, 2.50 ECTS


Course content

Part 1: Swarm intelligence
- Basics of swarm intelligence
- Examples of swarm-intelligent systems
- Basics of particle swarm optimization
- Conception and programming of swarm-intelligent models using agent-based programming
- Evaluation of swarm-intelligent models / simulations
Part 2: Genetic and evolutionary algorithms
- Basic principles of genetic and evolutionary algorithms
- Applications of genetic and evolutionary algorithms
- Use of evolutionary algorithms to evaluate agent-based models
- Basic principles of evolutionary game theory
- Basic principles of artificial immune systems

Learning outcomes

The students are able to independently plan, create or program swarm-intelligent models and to evaluate simulation results in this regard. They are also familiar with the most important principles and applications of genetic and evolutionary algorithms. The students are also familiar with the basic principles of evolutionary game theory and artificial immune systems.

Recommended or required reading and other learning resources / tools

Recommended literature or books:
- Binmore, K. G. (1991). Fun and Games: A Text on Game Theory. Great Source Education Gr, 1st edition.
- Engelbrecht, A. P. (2007).Computational Intelligence: An Introduction. Wiley, 2nd edition.
- Fudenberg, D., Tirole, J. (1991). Game Theory (Mit Press). The MIT Press, 1st edition.
- Gaertner, W. (2009). A Primer in Social Choice Theory (LSE Perspectives in Economic Analysis). Oxford University Press, 1st edition (revised edition).
- Gibbons, R. (1992). Primer In Game Theory. Financial Times / Prentice Hall, 1st edition.
- Gilbert N. (2008). Agent-based Models. Series: Quantitative Applications in the Social Sciences 153. Sage Publications, 2nd edition.
- Kelly, J. (2012). Social Choice Theory. Springer, 1st edition (softcover reprint).
- Kreps, D. M. (1990). Course Microeconomic Theory. Financial Times Prent.Int, 1st edition.
- Kruse, R., Borgelt, C. et al (2015). Computational Intelligence: Eine methodische Einführung in Künstliche Neuronale Netze, Evolutionäre Algorithmen, Fuzzy-Systeme und Bayes-Netze. Springer Vieweg, 2. Auflage.
- Luce, R. D., Raiffa, H. (1990). Games and Decisions: Introduction and Critical Survey (Dover Books on Mathematics). Dover Publications Inc., 1st edition (New edition).
- Mas-Colell, A. (1995). Microeconomic Theory. Oxford University Press, 1st edition.
- Railsback S. F., Grimm V. (2012). Agent-Based and Individual-Based Modeling. Princeton University Press, 2nd edition.
- Salanie, B. (2017). The Economics of Contracts: A Primer, 2nd Edition (Mit Press). The MIT Press, 2nd edition.
- Sayama, H. (2015). Introduction to the Modeling and Analysis of Complex Systems. Open SUNY Textbooks, 1st edition (print edition).
- Watson, J. (2013). Strategy: An Introduction to Game Theory. W W NORTON & CO, 1st edition (revised).
- Weicker, K. (2015). Evolutionäre Algorithmen. Springer Vieweg, 3rd edition.
- Wilensky U., Rand W. (2015). An Introduction to Agent-Mased Modeling. Modeling Natural, Social, and Engineered Complex Systems with NetLogo. MIT Press, 1st edition.
Recommended journals or selected articles:
- Diamond, J. M. (2002): Life with the artificial Anasazi. Nature, Vol. 419, 10 October 2002, S. 567-569.
- Grimm V. et al. (2006): A standard protocol for describing individual-based and agent-based models. Ecological Modelling 198, Elsevier, S. 115-126.
- Grimm V. et al. (2010): The ODD protocol: A review and first update. Evological Modelling 221, Elsevier, S. 2760-2768.
- Janssen, M. A. (2009): Understanding Articial Anasazi. In: Journal of Artificial Societies and Social Simulation, 12/4/13, 2009.
- Railsback, S., Ayllon, D. et al. (2017): Improving Execution Speed of Models Implemented in NetLogo. In: Journal of Artificial Societies and Social Simulation20(1) 3, 2017
- Stonedahl, F. and Wilensky, U. (2010): Evolutionary Robustness Checking in the Artificial Anasazi Model. In: Association for the Advancement of Artificial Intelligence, 2010.

Other relevant journals and articles will be announced in the courses.

Typical software for this module:
Python/Spyder/PyCharm, Matlab/Octave/Scilab, Gambit, NetLogo/BehaviorSearch, R/RStudio etc.

Mode of delivery

1,25 ECTS Lecture, 1,25 ECTS Exercise

Prerequisites and co-requisites

Module 9, 10 and 12

Assessment methods and criteria

Lecture: final exam; Exercise: examination character