Title:

Applied Evolutionary Algorithms

Code:EVO
Ac.Year:2017/2018
Term:Summer
Curriculums:
ProgrammeBranchYearDuty
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:Czech
Public info:http://www.fit.vutbr.cz/study/courses/EVO/public/
Credits:5
Completion:examination (written)
Type of
instruction:
Hour/semLecturesSem. ExercisesLab. exercisesComp. exercisesOther
Hours:26001218
 ExaminationTestsExercisesLaboratoriesOther
Points:60001822
Guarantee: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 competences:
  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, principles of stochastic search algorithms.
  2. Basic evolutionary algorithms (evolutionary programming, evolution strategies).
  3. Genetic algorithms (principles, parameters, genetic operators).
  4. Genetic programming (principles, symbolic regression)
  5. Case studies: desihn of sorting networks, evolution of cellular automata.
  6. Numerical optimization, differential evolution.
  7. Social computing algorithms (Particle Swarm Optimization, Ant Colony Algorithms).
  8. Advanced estimation distribution algorithms.
  9. Evolutionary development, grammatical evolution.
  10. Multiobjective evolutionary algorithms.
  11. Parallel evolutionary algorithms.
  12. Coevolutionary algorithms.
  13. Other selected nature-inspired paradigmas.
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 individual selected topic either by:
  • implementing a given application from the field of evolutionary computation or
  • study of a given paper, presentation of main ideas.
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.