Course detail

Computational Intelligence

FSI-9VINAcad. year: 2023/2024

Computational Intelligence covers a set of nature-inspired computational methodologies and approaches to address complex real-world problems to which mathematical or traditional modeling can be useless. The course introduces basic approaches and advance methods used in the field. Practical use of the methods is demonstrated on solving simple engineering problems. Students will be given time to practice of own optimization tasks.

Language of instruction


Number of ECTS credits


Mode of study

Not applicable.

Entry knowledge

The knowledge of basic relations of the optimization and statistics.

Rules for evaluation and completion of the course

Submitting and defence the project which present/uses implementation of selected CI method.
The attendance at lectures is recommended. Education runs according to individual schedules. The form of compensation of missed seminars is fully in the competence of the tutor.


To give students knowledge of Computational Intelligence fundamentals, i.e. of fundamentals of nature-inspired approaches to solving hard real-world problems. Namely of fundamentals for solving of optimization problems, mathematical models and classification. The various evolutionary algorithms, optimization metaheuristics and artificial neural networks will be presented.
Understanding of basic methods of Computational Intelligence and ability of their implementation.

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Aliev,R.A, Aliev,R.R.: Soft Computing and its Application, World Scientific Publishing Co. Pte. Ltd., 2001, ISBN 981-02-4700-1 (EN)
Sima,J., Neruda,R.: Theoretical questions of neural networks, MATFYZPRESS, 1996, ISBN 80-85863-18-9 (CS)

Recommended reading

Not applicable.

Classification of course in study plans

  • Programme D-APM-K Doctoral, 1. year of study, winter semester, recommended
  • Programme D-APM-P Doctoral, 1. year of study, winter semester, recommended

Type of course unit



20 hours, optionally

Teacher / Lecturer


The lectures are divided into four blocks:
Block 1: Relationship between Computational Intelligence and Artificial Intelligence. Presentation of engineering tasks. Presentation of student tasks.
Block 2: Evolutionary algorithms, optimisation metaheuristics, swarm intelligence (Genetics Algorithms, Grammatical Evolution, Genetic Programming, Ant Colony Optimisation, metaheuristics HC12).
Block 3: Artificial Neural Networks (feedforward neural networks, recurrent neural networks, self-organisation, deep learning)
Block 4: Individual consultations for own tasks.