Course details

Applied Evolutionary Algorithms

EVA Acad. year 2004/2005 Summer semester 6 credits

Current academic year

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.

Guarantor

Schwarz Josef, doc. Ing., CSc.

Language of instruction

Czech

Completion

Examination

Time span

  • 39 hrs lectures
  • 12 hrs pc labs
  • 14 hrs projects

Department

Department of Computer Systems (UPSY)

Subject specific learning outcomes and competences

  • 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++.

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.

Prerequisite knowledge and skills

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

Study literature

  • 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.

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 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.
  • stránky EVONET

Syllabus of lectures

  1. Evolutionary algorithms, basic classification. Optimization tasks.
  2. Genetic algorithms (GA), schema theorem.
  3. Advanced genetic algorithms, diploids, messy-chromozomes.
  4. Combinatorial tasks. Evolution strategies.
  5. Evolution programming. Genetic programming.
  6. Simulated annealing. Hill climbing algorithms. Tabu search.
  7. Evolutionary algorithms with probabilistic models (EDA algorithms).
  8. Variants of EDA algorithms - UMDA, BMDA, BOA.
  9. Multimodal and multiobjective tasks.
  10. Dynamical optimization tasks. Immune systems.
  11. Hybrid genetic algorithms. Techniques for fast prototyping.
  12. Synergy of genetic algorithms, fuzzy logic and neural networks. Classifiers.
  13. Typical problems in engineering practice.

 

Progress assessment

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

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Controlled instruction

There are no checked study.

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