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Course, academic year 2018/2019
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Evolutionary Algorithms II - NAIL086
Title in English: Evoluční algoritmy II
Guaranteed by: Department of Theoretical Computer Science and Mathematical Logic (32-KTIML)
Faculty: Faculty of Mathematics and Physics
Actual: from 2015 to 2019
Semester: summer
E-Credits: 6
Hours per week, examination: summer s.:2/2 C+Ex [hours/week]
Capacity: unlimited
Min. number of students: unlimited
State of the course: taught
Language: Czech, English
Teaching methods: full-time
Guarantor: Mgr. Roman Neruda, CSc.
Class: Informatika Mgr. - Teoretická informatika
Classification: Informatics > Theoretical Computer Science
Co-requisite : NAIL025
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 - Czech
Last update: T_KTI (26.05.2008)

Naučit vybrané pokročilé části z teorie evolučních algoritmů a jejich aplikace.

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

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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