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FIT-EVOAcad. year: 2021/2022
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
Department
Learning outcomes of the course unit
Prerequisites
Co-requisites
Recommended optional programme components
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
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
Language of instruction
Work placements
Aims
Get a survey of 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.
Specification of controlled education, way of implementation and compensation for absences
Computer practices, project submission, final exam.
Classification of course in study plans
branch MBI , any year of study, summer semester, 5 credits, compulsory-optionalbranch MPV , any year of study, summer semester, 5 credits, compulsory-optionalbranch MGM , any year of study, summer semester, 5 credits, electivebranch MSK , any year of study, summer semester, 5 credits, electivebranch MIS , any year of study, summer semester, 5 credits, electivebranch MBS , any year of study, summer semester, 5 credits, electivebranch MIN , any year of study, summer semester, 5 credits, electivebranch MMM , any year of study, summer semester, 5 credits, elective
specialization NADE , any year of study, summer semester, 5 credits, electivespecialization NBIO , any year of study, summer semester, 5 credits, electivespecialization NGRI , any year of study, summer semester, 5 credits, electivespecialization NNET , any year of study, summer semester, 5 credits, electivespecialization NVIZ , any year of study, summer semester, 5 credits, electivespecialization NCPS , any year of study, summer semester, 5 credits, electivespecialization NSEC , any year of study, summer semester, 5 credits, electivespecialization NEMB , any year of study, summer semester, 5 credits, electivespecialization NHPC , any year of study, summer semester, 5 credits, electivespecialization NISD , any year of study, summer semester, 5 credits, electivespecialization NIDE , any year of study, summer semester, 5 credits, electivespecialization NISY do 2020/21 , any year of study, summer semester, 5 credits, electivespecialization NISY , any year of study, summer semester, 5 credits, electivespecialization NMAL , any year of study, summer semester, 5 credits, electivespecialization NMAT , any year of study, summer semester, 5 credits, electivespecialization NSEN , any year of study, summer semester, 5 credits, electivespecialization NVER , any year of study, summer semester, 5 credits, electivespecialization NSPE , any year of study, summer semester, 5 credits, elective
Lecture
Teacher / Lecturer
Syllabus
Exercise in computer lab
Project
Realisation of individual topics from the area of evolutionary computation.
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