Title:

Applied Evolutionary Algorithms

Code:EVO
Ac.Year:2014/2015
Term: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-2MPV2ndCompulsory-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:2600818
 ExamsTestsExercisesLaboratoriesOther
Points:51180823
Guarantor:Schwarz Josef, doc. Ing., CSc. (DCSY)
Lecturer:Schwarz Josef, doc. Ing., CSc. (DCSY)
Instructor:Petrlík Jiří, 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 make students familiar with software tools for fast prototyping of evolutionary algorithms and learn how to solve typical complex tasks from engineering practice.
Description:
  Multiobjective optimization problems, standard approaches and stochastic evolutionary algorithms (EA), simulated annealing (SA). Evolution strategies (ES) and genetic algorithms (GA). Tools for fast prototyping. Representation of problems by graph models. Evolutionary algorithms in engineering applications namely in synthesis and physical design of digital circuits, artificial intelligence, signal processing, scheduling in multiprocessor systems and in business commercial applications.
Learning outcomes and competences:
  Ability of problem formulation for the solution on the base of evolutionary computation. Knowledge of methodology of fast prototyping of evolutionary optimizer utilizing GA library and present design tools.
Syllabus of lectures:
 
  • Evolutionary algorithms, theoretical foundation, basic distribution (GA, EP,GP, ES).
  • Genetic algorithms (GA), schemata theory.
  • Genetic algorithms using diploids and messy-chromosomes. Specific crossing.
  • Representative combinatorial optimization problems.
  • Evolutionary programming, Hill climbing algorithm, Simulated annealing. 
  • Genetic programming.
  • Advanced estimation distribution algorithms (EDA).
  • Variants of EDA algorithms, UMDA, BMDA and BOA.
  • Multimodal and multi-criterial optimization.
  • Dynamic optimization problems.
  • New evolutionary paradigm: immune systems,  differential evolution, SOMA.
  • Differential evolution. Particle swarm model. 
  • Engineering tasks and evolutionary algorithms.

 

Syllabus of laboratory exercises:
 
  • Simple design of an optimizer with GADesign system.
  • Utilizing of GA libraries like GAlib.
  • Genetic programming in Java.
  • Illustration of the program BMDA.

 

Syllabus - others, projects and individual work of students:
 
  • Program for the optimization of given problem on the base of evolutionary computation.
Fundamental literature:
 
  • Kvasnička V., Pospíchal J.,Tiňo P.: Evolutionary algorithms. Publisher STU Bratislava, 2000, pp. 215, ISBN 80-227-1377-5.
  • Dasgupta D., Michalewicz Z.: Evolutionary algorithms in engineering applications. Springer Verlag, Berlin, 1997, ISBN 3-540-62021-4.
  • Back, J: Evolutionary algorithms, theory and practice, New York, 1996.
Study literature:
 
  • Kvasnička V., Pospíchal J.,Tiňo P.: Evolutionary algorithms. Publisher STU Bratislava, 2000, pp. 215, ISBN 80-227-1377-5.
  • Kvasnička V., a kol.: Introduction into theory of neural networks, Iris 1997, ISBN 80-88778-30-1.
Progress assessment:
  Midterm and final test, one project.
 

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