Course detail

Applied Evolutionary Algorithms

FIT-EVOAcad. year: 2021/2022

Overview of principles of stochastic search techniques: Monte Carlo (MC) methods, evolutionary algorithms (EAs). Detailed explanation of selected MC algorithms: Metropolis algorithm, simulated annealing, their application for optimization and simulation. Overview of basic principles of EAs: evolutionary programming (EP), evolution strategies (ES), genetic algorithms (GA), genetic programming (GP).  Advanced EAs and their applications: numerical optimization, differential evolution (DE), social algoritmhs: ant colony optimization (ACO) and particle swarm optimization (PSO). Multiobjective optimization algorithms. Applications in solving engineering problems and artificial intelligence.

Language of instruction

Czech

Number of ECTS credits

Mode of study

Not applicable.

Guarantor

doc. Ing. Michal Bidlo, Ph.D.

Department

Department of Computer Systems (UPSY)

Learning outcomes of the course unit

Ability of problem formulation for the solution on the base of evolutionary computation. Knowledge of analysis and design methods for evolutionary algorithms.

Prerequisites

Not applicable.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

Evaluated practices, project. In the case of a reported barrier preventing the student to perform scheduled activity, the guarantor can allow the student to perform this activity on an alternative date.

Course curriculum

Not applicable.

Work placements

Not applicable.

Aims

Get a survey of actual optimization techniques and evolutionary algorithms for solution of complex, NP complete problems. To learn how to solve typical complex tasks from engineering practice using evolutionary techniques.

Specification of controlled education, way of implementation and compensation for absences

Computer practices, project submission, final exam.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Bäck, T.: Evolutionary Algorithms in Theory and Practice. Oxford University Press, Oxford, 1996, ISBN 978-0195099713
Brabazon, A., O'Neill, M., McGarraghy, S.: Natural Computing Algorithms. Springer-Verlag Berlin Heidelberg, 2015, ISBN 978-3-662-43630-1
Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing, 2nd ed. Springer-Verlag Berlin Heidelberg, 2015, ISBN 978-3-662-44873-1
Jansen, T.: Analyzing Evolutionary Algorithms. Springer-Verlag, Berlin Heidelberg, 2013, ISBN 978-3-642-17338-7

Recommended reading

Brabazon, A., O'Neill, M., McGarraghy, S.: Natural Computing Algorithms. Springer-Verlag Berlin Heidelberg, 2015, ISBN 978-3-662-43630-1
Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing, 2nd ed. Springer-Verlag Berlin Heidelberg, 2015, ISBN 978-3-662-44873-1
Kvasnička, V., Pospíchal, J., Tiňo, P.: Evolučné algoritmy. STU Bratislava, Bratislava, 2000, ISBN 80-227-1377-5
Luke, S.: Essentials of Metaheuristics. Lulu, 2015, ISBN 978-1-300-54962-8
Talbi, E.-G.: Metaheuristics: From Design to Implementation. Wiley, Hoboken, New Jersey, 2009, ISBN 978-0-470-27858-1

Elearning

eLearning: currently opened course

Classification of course in study plans

  • Programme IT-MSC-2 Master's

    branch MBI , 0 year of study, summer semester, compulsory-optional
    branch MBS , 0 year of study, summer semester, elective
    branch MGM , 0 year of study, summer semester, elective
    branch MIN , 0 year of study, summer semester, elective
    branch MIS , 0 year of study, summer semester, elective
    branch MMM , 0 year of study, summer semester, elective
    branch MPV , 0 year of study, summer semester, compulsory-optional
    branch MSK , 0 year of study, summer semester, elective

  • Programme MITAI Master's

    specialization NADE , 0 year of study, summer semester, elective
    specialization NBIO , 0 year of study, summer semester, elective
    specialization NCPS , 0 year of study, summer semester, elective
    specialization NEMB , 0 year of study, summer semester, elective
    specialization NGRI , 0 year of study, summer semester, elective
    specialization NHPC , 0 year of study, summer semester, elective
    specialization NIDE , 0 year of study, summer semester, elective
    specialization NISD , 0 year of study, summer semester, elective
    specialization NMAL , 0 year of study, summer semester, elective
    specialization NMAT , 0 year of study, summer semester, elective
    specialization NNET , 0 year of study, summer semester, elective
    specialization NSEC , 0 year of study, summer semester, elective
    specialization NSEN , 0 year of study, summer semester, elective
    specialization NSPE , 0 year of study, summer semester, elective
    specialization NVER , 0 year of study, summer semester, elective
    specialization NVIZ , 0 year of study, summer semester, elective
    specialization NISY up to 2020/21 , 0 year of study, summer semester, elective
    specialization NISY , 0 year of study, summer semester, elective

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

doc. Ing. Michal Bidlo, Ph.D.
Ing. Jakub Husa, Ph.D.

Syllabus

  1. Principles of stochastic search algorithms.
  2. Monte Carlo methods.
  3. Evolutionary programming and evolution strategies.
  4. Genetic algorithms.
  5. Genetic programming.
  6. Models of computational development.
  7. Statistical evaluation of experiments.
  8. Ant colony optimization.
  9. Particle swarm optimization.
  10. Differential evolution.
  11. Applications of evolutionary algorithms.
  12. Fundamentals of multiobjective optimization.
  13. Advanced algorithms for multiobjective optimization.

Exercise in computer lab

12 hod., optionally

Teacher / Lecturer

Ing. Václav Šimek

Syllabus

  1. Basic concepts of evolutionary computing, typical problems, solution of a technical task using a variant of Metropolis algorithm.
  2. Evolutionary algorithms in engineering areas, optimization of electronic circuits using genetic algorithm.
  3. Evolutionary design using genetic programming.
  4. Edge detection based on ant algorithms.
  5. Differential evolution-based optimization of neural networks.
  6. Solution of a selected task from statistical physics.

Project

14 hod., optionally

Teacher / Lecturer

Ing. Jakub Husa, Ph.D.
doc. Ing. Michal Bidlo, Ph.D.

Syllabus

Realisation of individual topics from the area of evolutionary computation.

Elearning

eLearning: currently opened course