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Natural computing for learning and optimisation - NPFL132

Title:	Přírodní algoritmy učení a optimalisace
Guaranteed by:	Institute of Formal and Applied Linguistics (32-UFAL)
Faculty:	Faculty of Mathematics and Physics
Actual:	from 2023
Semester:	winter
E-Credits:	4
Hours per week, examination:	winter s.:2/1, Ex [HT]
Capacity:	unlimited
Min. number of students:	unlimited
4EU+:	no
Virtual mobility / capacity:	no
State of the course:	taught
Language:	Czech, English
Teaching methods:	full-time
Teaching methods:	full-time
Additional information:	https://ufal.mff.cuni.cz/courses/npfl132
Note:	course can be enrolled in outside the study plan

Guarantor:	Mgr. Nino Peterek, Ph.D.
Incompatibility :	NPFL107
Interchangeability :	NPFL107
Is incompatible with:	NPFL107
Is interchangeable with:	NPFL107

Opinion survey results Examination dates WS schedule Noticeboard

Annotation -

Last update: RNDr. Jiří Mírovský, Ph.D. (24.05.2021)

The course offers introduction into some parts of nature-inspired computing. The topics of the course are self-organisation in nature and artificial systems, swarm intelligence algorithms, social insects colonies organisation. Organisms can co-operate to achieve certain tasks, their methods are effective in general optimisation and learning tasks. The aim of the course is to show a collection of these algorithms, and examine their components and their behavior.

Course completion requirements -

Last update: RNDr. Jiří Mírovský, Ph.D. (24.05.2021)

Presentation of own implementation of two selected algorithms from the course.

Literature -

Last update: RNDr. Jiří Mírovský, Ph.D. (24.05.2021)

D. Corne, A. Reynolds, E. Bonabeau (2010). Swarm Intelligence, in Handbook of Natural Computing (G. Rozenberg, T. Back, J.N. Kok, eds.), vol. II: Broader Perspective. Springer

D. W. Corne, K. Deb, J. Knowles, X. Yao (2010). Selected Applications of Natural Computing, (G. Rozenberg, T. Back, J.N. Kok, eds.), vol. II: Broader Perspective. Springer

Blum, C. and Li, X. , Swarm Intelligence in Optimization, in Blum, C. and Merkle, D. (eds.), Swarm Intelligence - Introduction and Applications, Springer, 2008: 43-85, 2008

M. Dorigo, M. Birattari, and T. Stützle (2006). Ant Colony Optimization: Artificial Ants as a Computational Intelligence Technique, IEEE Computational Intelligence Magazine, November:28-39.

X.S. Yang and S. Deb (2010). Engineering Optimisation by Cuckoo Search, International Journal of Mathematical Modelling and Numerical Numerical Optimisation, 1(4):330-343.

C.-R. Wang, C.-L. Zhou, and J.-W. Ma (2005). An Improved Artificial Fish-Swarm Algorithm and Its Application in Feed-forward Neural Networks, Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, vol. 5:2890-2894

Omidvar, M., Li, X., and Yao, X. , Smart Use of Computational Resource Based on Contribution for Cooperative Co-evolutionary Algorithms, in Proceedings of Genetic and Evolutionary Computation Conference (GECCO'11), ACM Press: 1115-1122, 2011

Requirements to the exam -

Last update: RNDr. Jiří Mírovský, Ph.D. (24.05.2021)

Implementation of two selected algorithms from the course.

Syllabus -

Last update: RNDr. Jiří Mírovský, Ph.D. (24.05.2021)

Self-Organisation

Self-organisation in nature, physics, chemistry, biology, mathematics,

computer science, linguistics, human society.

Swarm intelligence algorithms

Ant colony optimisation, the bacterial foraging algorithm, particle swarm

optimisation, the bee colony algorithm, cuckoo search, the firefly

algorithm.

Theory and applications

Multiobjective optimisation, particle trajectories, multimodal

optimisation, optimisation in a dynamic environment, co-evolutionary PSO,

current trends and related topics.