Title:

Applied Evolutionary Algorithms

Code:EVO
Ac.Year:2018/2019
Sem:Summer
Curriculums:
ProgrammeFieldYearDuty
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:Hyrš Martin, 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.St.G.EndG.
WedlecturelecturesD020712:0013:501MITxxxx
WedlecturelecturesD020712:0013:502MITxxxx
 
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 methods, evolutionary algorithms. Detailed explanation of selected algorithms: Metropolis algorithm, simulated annealing, problems in statistical physics. Overview of basic principles of evolutionary algorithms (EA): evolutionary programming (EP), evolution strategies (ES), genetic algorithms (GA), genetic programming (GP), differential evolution (DE). Advanced evolutionary techniques: estimation of distribution algorithms (EDA), multiobjective optimization, parallel and distributed EA. Social computing algoritmhs: particle swarm optimization (PSO), ant colony optimization (ACO). Applications in 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. Introduction, terminology, principles of stochastic search algorithms.
  2. Monte Carlo method and variants (Metropolis algorithm, Simulated Annealing).
  3. Basic evolutionary algorithms (Evolutionary Programming, Evolution Strategies).
  4. Genetic algorithms (control parameters, genetic operators).
  5. Genetic programming (representation, symbolic regression, applications).
  6. Evolutionary development and grammatical evolution.
  7. Numerical optimization (Particle Swarm Optimization, Differential Evolution).
  8. Ant Colony Optimization (basic principles, Ant System, Ant Colony System).
  9. Advanced evolutionary techniques (Estimation of Distribution Algorithms).
  10. Basic statistics for evolutionary computation.
  11. Evaluation of evolutionary experiments.
  12. Multiobjective optimization (basic techniques, engineering case study).
  13. Advanced multiobjective evolutionary algorithms. Summary of the course.
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.
  • Differential evolution-based optimization of neural networks.
  • Edge detection based on ant algorithms.
  • Solution of selected task from statistical physics.
Syllabus - others, projects and individual work of students:
 Conducting and evaluation of experiment regarding application of evolutionary algorithms to solve a selected task.

By agreement there is a possibility to include solution of the project from other course (e.g. BIN) to EVO if its topic belongs to 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.
Exam prerequisites:
  None.
 

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