Title:  Applied Evolutionary Algorithms 

Code:  EVA 

Ac.Year:  ukončen 2004/2005 

Sem:  Summer 

Language of Instruction:  Czech 

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

Credits:  6 

Completion:  examination (written) 

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

Hours:  39  0  0  12  14 

 Exams  Tests  Exercises  Laboratories  Other 

Points:  50  20  0  0  30 



Guarantor:  Schwarz Josef, doc. Ing., CSc. (DCSY) 

Lecturer:  Schwarz Josef, doc. Ing., CSc. (DCSY) 
Instructor:  Sekanina Lukáš, prof. Ing., Ph.D. (DCSY) 

Faculty:  Faculty of Information Technology BUT 

Department:  Department of Computer Systems FIT BUT 

Prerequisites:  

 Learning objectives: 

  To understand the paradigm of evolutionary algorithms including genetic algorithm (GA), evolution strategies (ES) and genetic programming (GP). To acquaint students with solving complex mostly NP complete optimization problems on the basis of conventional evolutionary algorithms and advanced evolutionary algorithms (EDA) which are based on the distribution of promising solutions. To acquaint students with programming tools for rapid prototyping of evolutionary algorithms for the solutions of technical tasks and problems from the area of artificial intelligence.  Description: 

  Theoretical and practical foundation of evolutionary computation. Genetic algorithms, evolution strategies, evolutionary programming, genetic programming and classifiers for the solution of multimodal and multiobjective tasks. Techniques for fast prototyping of genetic algorithms. Advanced estimation distribution algorithms (EDA). Synergy of evolutionary computation, fuzzy logic and neural networks. Evolutionary algorithms in engineering applications, artificial intelligence, knowledge base systems, VLSI design and multiprocessor tasks 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 competencies: 

   Students are capable to analyze the problem and to specify its complexity. Students are able to choose proper evolutionary techniques and find an adequate encoding of the solution for the given task.
 Students known how to specify suitable genetic operators and control parameters of evolutionary process including the choice of the population size, rate of crossing and mutation. The ability of the design and debugging of evolutionary algorithm for the given optimization problem on the platform C++.
 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.
 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.  
