Title:

# Applied Evolutionary Algorithms

Code:EVO
Ac.Year:2018/2019
Sem:Summer
Curriculums:
ProgrammeField/
Specialization
YearDuty
IT-MSC-2MBI-Compulsory-Elective - group I
IT-MSC-2MBS-Elective
IT-MSC-2MGM-Elective
IT-MSC-2MIN-Elective
IT-MSC-2MIS-Elective
IT-MSC-2MMI-Elective
IT-MSC-2MMM-Elective
IT-MSC-2MPV-Compulsory-Elective - group B
IT-MSC-2MSK-Elective
Language of Instruction:Czech
Public info:http://www.fit.vutbr.cz/study/courses/EVO/public/
Credits:5
Completion:examination (written)
Type of
instruction:
Hour/semLecturesSeminar
Exercises
Laboratory
Exercises
Computer
Exercises
Other
Hours:26001214
ExamsTestsExercisesLaboratoriesOther
Points:60001822
Guarantor:Bidlo Michal, Ing., Ph.D. (DCSY)
Lecturer:Bidlo Michal, Ing., Ph.D. (DCSY)
Instructor:Šimek Václav, Ing. (DCSY)
Faculty:Faculty of Information Technology BUT
Department:Department of Computer Systems FIT BUT
Substitute for:
 Applied Evolutionary Algorithms (EVA), DCSY
Schedule:
DayLessonWeekRoomStartEndLect.Gr.Groups
Thuexam - 2. oprava2019-06-06L321 09:0010:501MIT 2MIT
Thuexam - řádná2019-05-16E104 10:0011:501MIT 2MIT
Friexam - 1. oprava2019-05-31L314 09:0010:501MIT 2MIT

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.
Description:
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.
Learning outcomes and competencies:
Ability of problem formulation for the solution on the base of evolutionary computation. Knowledge of analysis and design methods for evolutionary algorithms.
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 of laboratory exercises:

• 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 selected task from statistical physics.
Syllabus - others, projects and individual work of students:
Realisation of individual topics from the area of evolutionary computation.

Fundamental 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
• 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
Study literature:

• Luke, S.: Essentials of Metaheuristics. Lulu, 2015, ISBN 978-1-300-54962-8
• Jansen, T.: Analyzing Evolutionary Algorithms. Springer-Verlag, Berlin Heidelberg, 2013, ISBN 978-3-642-17338-7
• 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
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.