Title:

Applied Evolutionary Algorithms

Code:EVO
Ac.Year:2017/2018
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:Šimek Václav, Ing. (DCSY)
Faculty:Faculty of Information Technology BUT
Department:Department of Computer Systems FIT BUT
Substitute for:
Applied Evolutionary Algorithms (EVA), DCSY
 
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 and symbolic regression.
  6. Case studies (design of algorithms and electronic circuits).
  7. Differential evolution (numerical optimization, engineering case study).
  8. Advanced evolutionary techniques (Estimation of Distribution Algorithms).
  9. Multiobjective evolutionary algorithms (basic techniques, engineering case study).
  10. Advanced multiobjective evolutionary algorithms.
  11. Parallel evolutionary algorithms and coevolutionary algorithms.
  12. Evolutionary development and grammatical evolution.
  13. Social computing algorithms (Particle Swarm Optimization, Ant algorithms).
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, estimation of distribution algorithms.
  • Optimization using social computing algorithms.
  • Solution of selected problems of statistical mechanics.
Syllabus - others, projects and individual work of students:
 Solution of a task selected from topics published for the actual academic year.

By agreement there is a possibility to accept the project from other courses (e.g. BIN) for EVO if its topic relates to evolutionary computation and the solution fulfils requirements of EVO projects.
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:
  6 computer practices (at most 3 points for each), a project with ongoing work defense and final defense (in summary for at most 22 points). In case of documented study impediments an additional term will be specified to substitute the missed practice(s).
Exam prerequisites:
  None.
 

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