Course detail
Applied Evolutionary Algorithms
FIT-EVOAcad. year: 2023/2024
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
Number of ECTS credits
Mode of study
Guarantor
Department
Entry knowledge
Rules for evaluation and completion of the course
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.
Aims
Ability of problem formulation for the solution on the base of evolutionary computation. Knowledge of analysis and design methods for evolutionary algorithms.
Study aids
Prerequisites and corequisites
Basic 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
Jansen, T.: Analyzing Evolutionary Algorithms. Springer-Verlag, Berlin Heidelberg, 2013, ISBN 978-3-642-17338-7
Recommended reading
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
Classification of course in study plans
- Programme IT-MSC-2 Master's
branch MPV , 0 year of study, summer semester, compulsory-optional
branch MBI , 0 year of study, summer semester, compulsory-optional
branch MBS , 0 year of study, summer semester, elective
branch MIS , 0 year of study, summer semester, elective
branch MIN , 0 year of study, summer semester, elective
branch MGM , 0 year of study, summer semester, elective
branch MSK , 0 year of study, summer semester, elective
branch MMM , 0 year of study, summer semester, elective - Programme MITAI Master's
specialization NISY , 0 year of study, summer semester, elective
specialization NSPE , 0 year of study, summer semester, elective
specialization NBIO , 0 year of study, summer semester, elective
specialization NSEN , 0 year of study, summer semester, elective
specialization NVIZ , 0 year of study, summer semester, elective
specialization NGRI , 0 year of study, summer semester, elective
specialization NADE , 0 year of study, summer semester, elective
specialization NISD , 0 year of study, summer semester, elective
specialization NMAT , 0 year of study, summer semester, elective
specialization NSEC , 0 year of study, summer semester, elective
specialization NISY up to 2020/21 , 0 year of study, summer semester, elective
specialization NCPS , 0 year of study, summer semester, elective
specialization NHPC , 0 year of study, summer semester, elective
specialization NNET , 0 year of study, summer semester, elective
specialization NMAL , 0 year of study, summer semester, elective
specialization NVER , 0 year of study, summer semester, elective
specialization NIDE , 0 year of study, summer semester, elective
specialization NEMB , 0 year of study, summer semester, elective
specialization NEMB up to 2021/22 , 0 year of study, summer semester, elective
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
- Principles of stochastic search algorithms.
- Monte Carlo methods.
- Evolutionary programming and evolution strategies.
- Genetic algorithms.
- Genetic programming.
- Differential evolution.
- Ant colony optimization.
- Statistical evaluation of experiments.
- Particle swarm optimization.
- Models of computational development.
- Fundamentals of multiobjective optimization.
- Advanced algorithms for multiobjective optimization.
- Applications of evolutionary algorithms.
Exercise in computer lab
Teacher / Lecturer
Syllabus
- Basic concepts of evolutionary computing, typical problems, solution of a technical task using a variant of Metropolis algorithm.
- Evolutionary algorithms in engineering areas, optimization of electronic circuits using genetic algorithm.
- Evolutionary design using genetic programming.
- Edge detection based on ant algorithms.
- Differential evolution-based optimization of neural networks.
- Solution of a selected task from statistical physics.
Project
Teacher / Lecturer
Syllabus
Elearning