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Course, academic year 2023/2024
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Evolutionary Algorithms 2 - NAIL086
Title: Evoluční algoritmy 2
Guaranteed by: Department of Theoretical Computer Science and Mathematical Logic (32-KTIML)
Faculty: Faculty of Mathematics and Physics
Actual: from 2020
Semester: summer
E-Credits: 5
Hours per week, examination: summer s.:2/2, C+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
Guarantor: Mgr. Roman Neruda, CSc.
Class: Informatika Mgr. - Teoretická informatika
Classification: Informatics > Theoretical Computer Science
Co-requisite : NAIL025
Incompatibility : NAIX086
Interchangeability : NAIX086
Is incompatible with: NAIX086
Is interchangeable with: NAIX086
Annotation -
Last update: Mgr. Roman Neruda, CSc. (02.05.2006)
Evolutionary programming, evolutionary strategies, genetic programming. Open-ended evolution and artificial life. Binary vs. float evolutionary algorithms, numerical optimization. Constraint handling, combinatorial optimization. Evolutionary learning of neural networks.
Aim of the course -
Last update: RNDr. Jan Hric (07.06.2019)


Course completion requirements -
Last update: RNDr. Jan Hric (07.06.2019)

Oral exam

Literature -
Last update: Mgr. Martin Pilát, Ph.D. (04.11.2019)

Mitchell, M.: Introduction to genetic algorithms. MIT Press, 1996.

Michalewicz, Z: Genetic algorithms + data structures = evolutionary programs. Springer Verlag, 1994.

Koza, J.: Genetic programming (I,II,III) MIT Press, 1992, 1994, 1996.

Chambers, L. (ed.): Practical handbook of genetic algorithms, CRC Press, 1995.

Syllabus -
Last update: Mgr. Roman Neruda, CSc. (02.05.2006)

Evolutionary programming, finite automata, meta-evolution.

Evolution Strategie, cooperation of individuals, 1+1, m+1 algorithms.

Genetic programming, LISP tree reprezentations, modularity.

Open-ended evolution, adaptive behavior, artificial life simulations, emergence (Tierra, Avida, Framsticks)

Numerical optimization, binary vs. float reprezentations, constraints handling.

Combinatorial problems, knapsack, travelling salesman, representations, operators.

Neural networks evolution, internal representation, topology, weights learning, hybrid algorithms.

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