Title:  Applied Evolutionary Algorithms 

Code:  EVA 

Ac.Year:  ukončen 2006/2007 (Not opened) 

Term:  Summer 

Curriculums:  

Language:  Czech 

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

Credits:  5 

Completion:  examination (written) 

Type of instruction:  Hour/sem  Lectures  Sem. Exercises  Lab. exercises  Comp. exercises  Other 

Hours:  26  0  0  12  14 

 Examination  Tests  Exercises  Laboratories  Other 

Points:  50  20  0  0  30 



Guarantee:  Schwarz Josef, doc. Ing., CSc., DCSY 

Lecturer:  Schwarz Josef, doc. Ing., CSc., DCSY 
Faculty:  Faculty of Information Technology BUT 

Department:  Department of Computer Systems FIT BUT 

Prerequisites:  


Learning objectives: 

  Survey about actual optimization techniques and evolutionary algorithms for solution of complex, NP complete problems. To make familiar students with software tools for fast prototyping of evolutionary algorithms and learn how to solve typical complex tasks in engineering practice. 
Description: 

  Theoretical and practical foundation of evolutionary computation. Evolutionary algorithms using genetic algorithms, evolution strategies, evolution programming, genetic programming and classifiers as probabilistic search algorithms. Techniques for fast prototyping of genetic algorithms. Advanced estimation distribution algorithms (EDA). Synergy of evolutionary computation and fuzzy logic. Evolutionary algorithms in engineering application: artificial intelligence, knowledge base systems, VLSI design and multiprocessor scheduling. 
Knowledge and skills required for the course: 

  Basic knowledge of algorithm theory and their complexity. Basic terms from graph theory, artificial intelligence and probability theory. 
Learning outcomes and competences: 

  Ability of problem formulation for the solution on the base of evolutionary computation. Knowledge of methodology for fast prototyping of evolutionary optimizer utilizing GA library and current design tools. 
Syllabus of lectures: 


 Evolutionary algorithms, basic classification. Optimization tasks.
 Genetic algorithms (GA), schema theorem.
 Advanced genetic algorithms, diploids, messychromozomes.
 Combinatorial tasks. Evolution strategies.
 Evolution programming. Genetic programming.
 Simulated annealing. Hill climbing algorithms. Tabu search.
 Evolutionary algorithms with probabilistic models (EDA algorithms).
 Variants of EDA algorithms  UMDA, BMDA, BOA.
 Multimodal and multiobjective tasks.
 Dynamical optimization tasks. Immune systems.
 Hybrid genetic algorithms. Techniques for fast prototyping.
 Synergy of genetic algorithms, fuzzy logic and neural networks. Classifiers.
 Typical problems in engineering practice.

Syllabus of laboratory exercises: 


 Presentation of the GADesign design tool.
 Optimizer design with the GADesign tool.
 Structure and utilizing of GALIB library.
 Optimizer design with the GALIB library.
 Presentation of the DEBOA design tool.
 Optimizer design with the DEBOA tool.

Syllabus  others, projects and individual work of students: 


 Program implementation for the solution of a given optimization problem by means of the evolutionary algorithms.

Fundamental literature: 


 Eiben, A. E., Smith, E.: Introduction to Evolutionary Computing (Natural Computing Series). Springer Verlag, November, 2003, pp. 299, ISBN 3540401849.
 Dasgupta, D., Michalewicz, Z.: Evolutionary algorithms in engineering applications. Springer Verlag, Berlin, 1997, ISBN 3540620214.
 Back, J: Evolutionary algorithms, theory and practice, New York, 1996.
 Kvasnička, V., Pospíchal, J.,Tiňo, P.: Evolutionary algorithms. Publisher STU Bratislava, 2000, pp. 215, ISBN 8022713775.

Study literature: 


 Kvasnička V., Pospíchal J., Tiňo P.: Evolutionary algorithms. Publisher STU Bratislava, 2000, pp. 215, ISBN 8022713775.
 Kvasnička V., a kol.: Introduction into theory of neural networks, Iris 1997, ISBN 8088778301.

Controlled instruction: 

  Project is monitored 
Progress assessment: 

   Midterm written exam  20 points.
 Individual project  30 points.
 Final written examination  50 points.
 Passing boundary for ECTS assessment  50 points.

Exam prerequisites: 

  Requirements for class accreditation are not defined. 
