Course details

Applied Evolutionary Algorithms

EVO Acad. year 2023/2024 Summer semester 5 credits

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.

Guarantor

Course coordinator

Language of instruction

Czech

Completion

Examination (written)

Time span

  • 26 hrs lectures
  • 12 hrs pc labs
  • 14 hrs projects

Assessment points

  • 60 pts final exam
  • 18 pts labs
  • 22 pts projects

Department

Lecturer

Instructor

Learning objectives

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.

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

Why is the course taught

Algorithms inspired by nature represent strong techniques for solving many difficult optimization tasks. Also, it has been shown for several times that evolutionary algorithms may provide innovative solutions to some problems which are out of the scope of existing methods. Therefore it is important to provide a course about evolutionary algorithms and their applications as a part of expertise to expectant engineers in the area of information technology.

Study literature

  • 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
  • Talbi, E.-G.: Metaheuristics: From Design to Implementation. Wiley, Hoboken, New Jersey, 2009, ISBN 978-0-470-27858-1
  • Luke, S.: Essentials of Metaheuristics. Lulu, 2015, ISBN 978-1-300-54962-8

Fundamental 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

Syllabus of lectures

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

Syllabus of computer exercises

  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.

Syllabus - others, projects and individual work of students

Realisation of individual topics from the area of evolutionary computation.

Progress assessment

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.

Computer practices, project submission, final exam.

Exam prerequisites

None.

How to contact the teacher

Schedule

DayTypeWeeksRoomStartEndCapacityLect.grpGroupsInfo
Mon other 2024-03-11 L307 14:0014:30 Konzultace projektu
Mon other 2024-03-18 L307 14:0014:30 Konzultace projektu
Mon other 2024-03-25 L307 14:0014:30 Konzultace projektu
Mon other 2024-04-01 L307 14:0014:30 Konzultace projektu
Mon other 2024-03-11 L307 14:3015:00 Konzultace projektu
Mon other 2024-03-18 L307 14:3015:00 Konzultace projektu
Mon other 2024-03-25 L307 14:3015:00 Konzultace projektu
Mon other 2024-04-01 L307 14:3015:00 Konzultace projektu
Mon other 2024-03-11 L307 15:0015:30 Konzultace projektu
Mon other 2024-03-18 L307 15:0015:30 Konzultace projektu
Mon other 2024-03-25 L307 15:0015:30 Konzultace projektu
Mon other 2024-04-01 L307 15:0015:30 Konzultace projektu
Mon other 2024-03-11 L307 15:3016:00 Konzultace projektu
Mon other 2024-03-18 L307 15:3016:00 Konzultace projektu
Mon other 2024-03-25 L307 15:3016:00 Konzultace projektu
Mon other 2024-04-01 L307 15:3016:00 Konzultace projektu
Mon other 2024-03-11 L307 16:0016:30 Konzultace projektu
Mon other 2024-03-18 L307 16:0016:30 Konzultace projektu
Mon other 2024-03-25 L307 16:0016:30 Konzultace projektu
Mon other 2024-04-01 L307 16:0016:30 Konzultace projektu
Mon other 2024-03-11 L307 16:3017:00 Konzultace projektu
Mon other 2024-03-18 L307 16:3017:00 Konzultace projektu
Mon other 2024-03-25 L307 16:3017:00 Konzultace projektu
Mon other 2024-04-01 L307 16:3017:00 Konzultace projektu
Tue lecture 1., 2., 3., 4., 5., 6., 7., 9., 10., 11., 12., 13. of lectures G202 08:0009:5080 1MIT 2MIT xx Bidlo
Tue lecture 2024-03-26 G202 08:0009:5080 1MIT 2MIT xx Husa
Tue comp.lab 2., 4., 6., 8., 10., 12. of lectures L306.1 L306.2 10:0011:5020 1MIT 2MIT xx Hurta
Tue comp.lab *) 2024-02-13 L306.1 L306.2 12:0013:5020 1MIT 2MIT xx Hurta Rezervní termín při navýšení počtu studentů
Wed comp.lab 2., 4., 6., 8., 10., 12. of lectures L306.1 L306.2 10:0011:5020 1MIT 2MIT xx Hurta
Fri other 2024-03-15 L307 14:0014:30 Konzultace projektu
Fri other 2024-03-22 L307 14:0014:30 Konzultace projektu
Fri other 2024-03-29 L307 14:0014:30 Konzultace projektu
Fri other 2024-04-05 L307 14:0014:30 Konzultace projektu
Fri other 2024-03-15 L307 14:3015:00 Konzultace projektu
Fri other 2024-03-22 L307 14:3015:00 Konzultace projektu
Fri other 2024-03-29 L307 14:3015:00 Konzultace projektu
Fri other 2024-04-05 L307 14:3015:00 Konzultace projektu
Fri other 2024-03-15 L307 15:0015:30 Konzultace projektu
Fri other 2024-03-22 L307 15:0015:30 Konzultace projektu
Fri other 2024-03-29 L307 15:0015:30 Konzultace projektu
Fri other 2024-04-05 L307 15:0015:30 Konzultace projektu
Fri other 2024-03-15 L307 15:3016:00 Konzultace projektu
Fri other 2024-03-22 L307 15:3016:00 Konzultace projektu
Fri other 2024-03-29 L307 15:3016:00 Konzultace projektu
Fri other 2024-04-05 L307 15:3016:00 Konzultace projektu
Fri other 2024-03-15 L307 16:0016:30 Konzultace projektu
Fri other 2024-03-22 L307 16:0016:30 Konzultace projektu
Fri other 2024-03-29 L307 16:0016:30 Konzultace projektu
Fri other 2024-04-05 L307 16:0016:30 Konzultace projektu
Fri other 2024-03-15 L307 16:3017:00 Konzultace projektu
Fri other 2024-03-22 L307 16:3017:00 Konzultace projektu
Fri other 2024-03-29 L307 16:3017:00 Konzultace projektu
Fri other 2024-04-05 L307 16:3017:00 Konzultace projektu
It is not possible to register this class in Studis. (Some exercises may be opened later if needed, but this is not guaranteed.)

Course inclusion in study plans

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