Title:  Evolutionary Computation 

Code:  EVD 

Ac.Year:  2017/2018 

Term:  Summer 

Curriculums:  

Language of Instruction:  Czech 

Private info:  http://www.fit.vutbr.cz/study/courses/EVD/private/ 

Completion:  examination (verbal) 

Type of instruction:  Hour/sem  Lectures  Seminar Exercises  Laboratory Exercises  Computer Exercises  Other 

Hours:  39  0  0  0  0 

 Exams  Tests  Exercises  Laboratories  Other 

Points:  51  0  0  0  49 



Guarantor:  Sekanina Lukáš, prof. Ing., Ph.D., DCSY 

Lecturer:  Sekanina Lukáš, prof. Ing., Ph.D., DCSY Schwarz Josef, doc. Ing., CSc., DCSY 
Faculty:  Faculty of Information Technology BUT 

Department:  Department of Computer Systems FIT BUT 

Schedule: 

Day  Lesson  Week  Room  Start  End  Lect.Gr.  St.G.  EndG. 

Thu  konzultace EVD  20180208  L314  13:00  13:50    
  Learning objectives: 

  To inform the students about up to date algorithms for solution of complex, NP complete problems.  Description: 

  Evolutionary computation in the context of artificial intelligence and optimization problems with NP complexity. Paradigm of genetic algorithms, evolutionary strategy, genetic programming and another evolutionary heuristics. Theory and practice of standard evolutionary computation. Advanced evolutionary algorithms based on graphic probabilistic models (EDA  estimation of distribution algorithms). Parallel evolutionary algorithms. A survey of representative applications of evolutionary algorithms in multiobjection optimization problems, artificial intelligence, knowledge based systems and digital circuit design. Techniques of rapid prototyping of evolutionary algorithms.  Learning outcomes and competences: 

  Skills and approaches in solution of hard optimization problems.  Syllabus of lectures: 


 Evolutionary algorithms, theoretical foundation, basic distribution.
 Genetic algorithms (GA), schemata theory.
 Advanced genetic algorithms
 Repesentative combinatorial optimization problems.
 Evolution strategies.
 Genetic programming.
 Advanced estimation distribution algorithms (EDA).
 Variants of EDA algorithms, UMDA, BMDA and BOA.
 Simulated annealing.
 Methods for multicriterial and multimodal problems. Selection and population replacement.
 Techniques for fast prototyping. Structure of development systems and GA library.
 New evolutionary paradigm: immune systems, differential evolution, SOMA.
 Typical application tasks.
 Syllabus  others, projects and individual work of students: 


 Defence of a project, software project based on a variant of evolutionary algorithm
 Fundamental literature: 


 Back, J: Evolutionary algorithms, theory and practice, New York, 1996.
 Goldberg, D., E.: The Design of Innovation: Lessons from and for Competent Genetic Algorithms. Boston, MA: Kluwer Academic Publishers, 2002. ISBN: 1402070985.
 Kvasnička V., Pospíchal J., Tiňo P.: Evolutionary algorithms. Publisher STU Bratislava, 2000, pp. 215, ISBN 8022713775.
 Study literature: 


 Fogel D., B.: Evolutionary computation: Toward a new philosophy of machine intelligence. IEEE Press, New York, 2000, ISBN 078035379X.
 Controlled instruction: 

  Project defence, software project based on a variant of evolutionary algorithms or the presentation of the assigned task.  
