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Evolutionary Algorithms I - NAIL025

Title:	Evoluční algoritmy I
Guaranteed by:	Department of Theoretical Computer Science and Mathematical Logic (32-KTIML)
Faculty:	Faculty of Mathematics and Physics
Actual:	from 2015 to 2019
Semester:	winter
E-Credits:	6
Hours per week, examination:	winter 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
Is co-requisite for:	NAIL086

Opinion survey results Examination dates Schedule Noticeboard

Annotation -

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

Models of evolution, genetic algorithms, representation and operators of selection, mutation and crossover. Problem solving by means of evolutionary computation. Theoretical properties of simple genetic algorithm. Schemata theorem and building block hypothesis, probabilistic models. Evolutionarz machine learning, Michigan vs. Pittsburg approach, classifier systems.

Aim of the course - Czech

Last update: T_KTI (26.05.2008)

Naučit základní techniky používané v evolučních algoritmech. Ukázat souvislosti s příbuznými oblastmi dataminingu a učení.

Literature - Czech

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

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

Goldberg, D.: Genetic algorithms in search optimization and machine learning, Addison-Wesley, 1989.

Holland, J.: Adaptation in natural and artificial systems, MIT Press, 1992 (2nd ed).

Holland, J.: Hidden order, Addison-Wesley, 1995.

Syllabus -

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

Models of evolution, basic approaches and notions. Population, recombination, fitness evaluation.

Genetic algorithms, solution encoding in a chromozome, basic operators of selection, mutation, crossover.

Selection, objective function, dynamic vs. static, roulette-wheel selection, tournaments, elitism.

Schema theorem, building block hypotheses, implicit paralallelism.

Probabilistic models of simple genetic algorithm, finite and infinite population.

Machine learning and data mining, evoluion of expert systems, internal representation, Michigan vs. Pittsburg approach.

Clasifier systems, if-then rules, bucket brigade algorithm, Q-learning, production systems.