Course detail

Applied Evolutionary Algorithms

FIT-EVOAcad. year: 2019/2020

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.

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.

Recommended optional programme components

Not applicable.

Recommended or required reading

Luke, S.: Essentials of Metaheuristics. Lulu, 2015, ISBN 978-1-300-54962-8
Kvasnička, V., Pospíchal, J., Tiňo, P.: Evolučné algoritmy. Vydavatelství STU Bratislava, Bratislava, 2000, ISBN 80-227-1377-5
Oplatková, Z., Ošmera, P., Šeda, M., Včelař, F., Zelinka, I.: Evoluční výpočetní techniky - principy a aplikace. BEN - technická literatura, Praha, 2008, ISBN 80-7300-218-3
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
Talbi, E.-G.: Metaheuristics: From Design to Implementation. Wiley, Hoboken, New Jersey, 2009, ISBN 978-0-470-27858-1
Bäck, T.: Evolutionary Algorithms in Theory and Practice. Oxford University Press, Oxford, 1996, ISBN 978-0195099713

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.
Exam prerequisites:
None.

Language of instruction

Czech

Work placements

Not applicable.

Aims

Survey about 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.

Classification of course in study plans

  • Programme IT-MGR-2 Master's

    branch MBI , any year of study, summer semester, 5 credits, compulsory-optional
    branch MPV , any year of study, summer semester, 5 credits, compulsory-optional
    branch MGM , any year of study, summer semester, 5 credits, optional
    branch MSK , any year of study, summer semester, 5 credits, optional
    branch MIS , any year of study, summer semester, 5 credits, optional
    branch MBS , any year of study, summer semester, 5 credits, optional
    branch MIN , any year of study, summer semester, 5 credits, optional
    branch MMI , any year of study, summer semester, 5 credits, optional
    branch MMM , any year of study, summer semester, 5 credits, optional

  • Programme MITAI Master's

    specialization NADE , any year of study, summer semester, 5 credits, optional
    specialization NBIO , any year of study, summer semester, 5 credits, optional
    specialization NGRI , any year of study, summer semester, 5 credits, optional
    specialization NNET , any year of study, summer semester, 5 credits, optional
    specialization NVIZ , any year of study, summer semester, 5 credits, optional
    specialization NCPS , any year of study, summer semester, 5 credits, optional
    specialization NSEC , any year of study, summer semester, 5 credits, optional
    specialization NEMB , any year of study, summer semester, 5 credits, optional
    specialization NHPC , any year of study, summer semester, 5 credits, optional
    specialization NISD , any year of study, summer semester, 5 credits, optional
    specialization NIDE , any year of study, summer semester, 5 credits, optional
    specialization NISY , any year of study, summer semester, 5 credits, optional
    specialization NMAL , any year of study, summer semester, 5 credits, optional
    specialization NMAT , any year of study, summer semester, 5 credits, optional
    specialization NSEN , any year of study, summer semester, 5 credits, optional
    specialization NVER , any year of study, summer semester, 5 credits, optional
    specialization NSPE , any year of study, summer semester, 5 credits, optional

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

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.

Computer exercise

12 hours, compulsory

Teacher / Lecturer

Projects

14 hours, compulsory

Teacher / Lecturer

Syllabus

Realisation of individual topics from the area of evolutionary computation.

eLearning