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Course, academic year 2018/2019
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Evolutionary Algorithms I - NAIL025
Title in English: 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 [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
Is co-requisite for: NAIL086
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.

Course completion requirements -
Last update: Mgr. Martin Pilát, Ph.D. (13.10.2017)

In order to pass the course, the student must obtain the credit for the seminar and pass an exam. The credit is given for solving assignments from the seminar. The nature of study verification excludes the possibility of its repetition.

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.

Requirements to the exam -
Last update: Mgr. Martin Pilát, Ph.D. (13.10.2017)

The exam is oral with time for written preparation. The requirements correspond to the syllabus in the extent presented during the lectures. A part of the exam asks to design an evolutionary algorithm for a given problem.

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.

 
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