Evolutionary Algorithms and Optimization (E)

LV 024913010503 Artificial Intelligence; Nonlinear Optimization, Linear Algebra, Analysis, Probability Theory and Statistics

• (1+1) evolution strategy (ES) and 1/5 rule
• Simulated annealing
• Multi-Membered ES
• Self-adaptation
• Cumulative step size adaptation
• Matrixadaptation-ES
• Empirical evaluation of black box search algorithms
• Introduction into progress rate and runtime analysis
• Genetic Algorithms
• Genetic Programming

The students

• are acquainted with basic methods and algorithms from the field of evolutionary learning and optimization.
• can apply and evaluate these algorithms and methods.
• are able to understand research papers in this field.

Lecture, laboratory exercises, literature work and seminar presentation if appropriate

Final exam character with an oral exam. Providing the laboratory exercises are prerequisites for the oral exam.

None

Use of Octave/MatLab.

• Beyer, Hans-Georg (2010): The Theory of Evolution Strategies. Softcover reprint of hardcover 1st ed. 2001 Edition. Berlin Heidelberg: Springer. Available at: URL: http://link.springer.com/10.1007/978-3-662-04378-3 (Accessed on: 21 December 2021).
• Eiben, A.E; Smith, James E (2015): Introduction to Evolutionary Computing. Available at: URL: https://doi.org/10.1007/978-3-662-44874-8 (Accessed on: 21 December 2021).
• Engelbrecht, Andries P. (2007): Computational Intelligence: An Introduction. 2nd Ed. Chichester, England ; Hoboken, NJ: Wiley.
• Weicker, Karsten (2015): Evolutionäre Algorithmen. 3., überarb. u. erw. Aufl. 2015 Edition. Wiesbaden: Springer Vieweg

Face-to-face event with recording of the lecture