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Course, academic year 2022/2023
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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 2021
Semester: winter
E-Credits: 4
Hours per week, examination: winter s.:2/1, Ex [HT]
Capacity: unlimited
Min. number of students: unlimited
Virtual mobility / capacity: no
State of the course: taught
Language: Czech, English
Teaching methods: full-time
Additional information: https://ufal.mff.cuni.cz/courses/npfl107
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
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.

 
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