This course provides the students with working knowledge of bio-inspired algorithms and their applications. It introduces the basic concepts of bio-inspired methods, briefly discusses their history and concentrates on the current state and recent developments in this field. The course first outlines the fundamental concepts of bio-inspired computation as such and then discusses the basic categories of bio-inspired methods including evolutionary computation, swarm intelligence, artificial neural networks, and hybrid methods. The students are also familiarized with different types of problems, typically solved by bio-inspired methods. In particular, continuous and discrete problems are discussed and bio-inspired methods, suitable for different types of problems, are discussed. Last but not least, the methods and techniques for the statistical evaluation and visualization of the results of bio-inspired algorithms are discussed.
The languages and frameworks for the practical design and implementation of bio-inspired methods in the scope of the course will include Python (scikit-learn), C/C++, and R (caret package).
Literature
• M. Affenzeller, S. Winkler, S. Wagner, A. Beham, Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications, Chapman & Hall/CRC, 2009.
• C. Blum, D. Merkle, Swarm Intelligence: Introduction and Applications, Springer Publishing Company, Incorporated, 2008.
• M. Clerc, Particle Swarm Optimization, ISTE, Wiley, 2010.
• M. Dorigo, T. Stützle, Ant Colony Optimization, MIT Press, Cambridge, MA, 2004.
• A. Engelbrecht, Fundamentals of Computational Swarm Intelligence, Wiley, New York, NY, USA, 2005.
Advised literature
• A. Engelbrecht, Computational Intelligence: An Introduction, 2nd Edition, Wiley, New York, NY, USA, 2007.