Course detail
Applied Evolutionary Algorithms
FIT-EVOAcad. year: 2020/2021
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.
Supervisor
Department
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. 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, elective
branch MSK , any year of study, summer semester, 5 credits, elective
branch MIS , any year of study, summer semester, 5 credits, elective
branch MBS , any year of study, summer semester, 5 credits, elective
branch MIN , any year of study, summer semester, 5 credits, elective
branch MMI , any year of study, summer semester, 5 credits, elective
branch MMM , any year of study, summer semester, 5 credits, elective - Programme MITAI Master's
specialization NADE , any year of study, summer semester, 5 credits, elective
specialization NBIO , any year of study, summer semester, 5 credits, elective
specialization NGRI , any year of study, summer semester, 5 credits, elective
specialization NNET , any year of study, summer semester, 5 credits, elective
specialization NVIZ , any year of study, summer semester, 5 credits, elective
specialization NCPS , any year of study, summer semester, 5 credits, elective
specialization NSEC , any year of study, summer semester, 5 credits, elective
specialization NEMB , any year of study, summer semester, 5 credits, elective
specialization NHPC , any year of study, summer semester, 5 credits, elective
specialization NISD , any year of study, summer semester, 5 credits, elective
specialization NIDE , any year of study, summer semester, 5 credits, elective
specialization NISY , any year of study, summer semester, 5 credits, elective
specialization NMAL , any year of study, summer semester, 5 credits, elective
specialization NMAT , any year of study, summer semester, 5 credits, elective
specialization NSEN , any year of study, summer semester, 5 credits, elective
specialization NVER , any year of study, summer semester, 5 credits, elective
specialization NSPE , any year of study, summer semester, 5 credits, elective
Type of course unit
Lecture
26 hours, optionally
Teacher / Lecturer
Syllabus
- Principles of stochastic search algorithms.
- Monte Carlo methods.
- Evolutionary programming and evolution strategies.
- Genetic algorithms.
- Genetic programming.
- Models of computational development.
- Statistical evaluation of experiments.
- Ant colony optimization.
- Particle swarm optimization.
- Differential evolution.
- Applications of evolutionary algorithms.
- Fundamentals of multiobjective optimization.
- Advanced algorithms for multiobjective optimization.
Project
14 hours, compulsory
Teacher / Lecturer
Syllabus
Realisation of individual topics from the area of evolutionary computation.