Evolutionary Algorithms II - NAIX086
Title: Evoluční algoritmy II
Guaranteed by: Student Affairs Department (32-STUD)
Faculty: Faculty of Mathematics and Physics
Actual: from 2022
Semester: summer
E-Credits: 6
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
Teaching methods: full-time
Teaching methods: full-time
Is provided by: NAIL086
Guarantor: Mgr. Roman Neruda, CSc.
Class: Informatika Mgr. - Teoretická informatika
Classification: Informatics > Theoretical Computer Science
Pre-requisite : {NXXX038, NXXX039, NXXX040, NXXX067, NXXX069}
Co-requisite : NAIL025, NAIX025
Incompatibility : NAIL086
Interchangeability : NAIL086
Is incompatible with: NAIL086
Is interchangeable with: NAIL086
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Annotation -
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.
Last update: Neruda Roman, Mgr., CSc. (02.05.2006)
Aim of the course -


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

Oral exam

Last update: Hric Jan, RNDr. (07.06.2019)
Literature -

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.

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

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.

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