Ing. Michal Bidlo, Ph.D.

Department of Computer Systems (UPSY)

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.

There are no prerequisites

Not applicable.

Not applicable.

• Kvasnička V., Pospíchal J., Tiňo P.: Evoluční algoritmy. Vydavatelství STU Bratislava, 2000, str. 215, ISBN 80-227-1377-5
• Kvasnička V., a kol.: Úvod do teorie neuronových sítí, Iris 1997, ISBN 80-88778-30-1.

• 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.
• Kvasnička V., Pospíchal J.,Tiňo P.: Evoluční algoritmy. Vydavatelství STU Bratislava, 2000, str. 215, ISBN 80-227-1377-5.

Not applicable.

Study evaluation is based on marks obtained for specified items. Minimimum number of marks to pass is 50.

Czech

Not applicable.

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:

• Implementation of 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.

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.

Midterm and final test, one project.

• Programme IT-MGR-2 Master's

  branch MBI , any year of study, summer semester, 5 credits, compulsory-optional
  branch MPV , any year of study, summer semester, 5 credits, compulsory-optional
  branch MGM , any year of study, summer semester, 5 credits, elective
  branch MSK , any year of study, summer semester, 5 credits, elective
  branch MIS , any year of study, summer semester, 5 credits, elective
  branch MBS , any year of study, summer semester, 5 credits, elective
  branch MIN , any year of study, summer semester, 5 credits, elective
  branch MMI , any year of study, summer semester, 5 credits, elective
  branch MMM , any year of study, summer semester, 5 credits, elective
  

18 hours, optionally

Ing. Michal Bidlo, Ph.D.

eLearning: currently opened course