Title:  Applied Evolutionary Algorithms 

Code:  EVO 

Ac.Year:  2009/2010 

Term:  Summer 

Curriculums:  

Language:  Czech 

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

Credits:  5 

Completion:  examination (written) 

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

Hours:  26  0  0  8  18 

 Examination  Tests  Exercises  Laboratories  Other 

Points:  50  18  0  8  24 



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

Lecturer:  Schwarz Josef, doc. Ing., CSc., DCSY 
Instructor:  Jaroš Jiří, Ing., Ph.D., DCSY Pospíchal Petr, Ing., DCSY 

Faculty:  Faculty of Information Technology BUT 

Department:  Department of Computer Systems FIT BUT 

Substitute for:  


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 messychromozomes. Specific crossing.
 Repesentative combinatorial optimization problems.
 Evolutionary programming, Hill cimbing algorithm, Simulated annealing.
 Genetic programming.
 Advanced estimation distribution algorithms (EDA).
 Variants of EDA algorithms, UMDA, BMDA and BOA.
 Multimodal and multicriterial optimization.
 Dynamoc optimization problems.
 New evolutionary paradigm: immune systems, differential evolution, SOMA.
 Differential evolution. Particle swarm model.
 Ingeneering 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 8022713775.
 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.

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.

Progress assessment: 

  Midterm and final test, one project. 
