Biological mechanisms can serve as inspiration for problem solving strategies and the design and management of complex computing systems. Characteristically, natural systems are self-organised, distributed, adaptive, and robust, which makes them interesting from the perspective of computer science. One
particularly important class of algorithms in context of biologically motivated problem solving techniques are evolutionary algorithms, which mainly borrow ideas from Darwinian evolution theory.
In this course, we will give an introduction to evolutionary algorithms and later discuss more advanced topics such as self-tuning algorithms, optimisation of time-varying environments, and constraint handling techniques. Further, we will compare evolutionary computation models such as genetic programming,
particle systems, and ant systems regarding their potential for applications in relation to these topics.
Lecturers:
Thiemo Krink and Rasmus K. Ursem
Literature
Z. Michalewicz: "Genetic Algorithms + DataStructures = Evolution
Programs", Springer, 1992.
Selected papers
Lecture notes
Prerequisites
dProg1 + dProg2
Course Language
English
Credits
2 points/10 ECTS credits