Course details

Applied Evolutionary Algorithms

EVO Acad. year 2019/2020 Summer semester 5 credits

Current academic year

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

Bidlo Michal, doc. Ing., Ph.D. (DCSY)

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 (written part)
  • 18 pts labs
  • 22 pts projects

Department

Department of Computer Systems (UPSY)

Lecturer

Bidlo Michal, doc. Ing., Ph.D. (DCSY)

Instructor

Husa Jakub, Ing. (DCSY)
Šimek Václav, Ing. (DCSY)

Course Web Pages

Subject specific learning outcomes and competences

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

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.

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

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. 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.

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.
Exam prerequisites:
None.

Exam prerequisites

None.

Course inclusion in study plans

Back to top