Course details

Applied Evolutionary Algorithms

EVA Acad. year 2005/2006 Summer semester 5 credits

Current academic year

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.

Guarantor

Schwarz Josef, doc. Ing., CSc.

Language of instruction

Czech

Completion

Examination

Time span

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

Department

Department of Computer Systems (UPSY)

Subject specific 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.

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.

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

  • Evolutionary algorithms, basic classification. Optimization tasks. 
  • Genetic algorithms (GA), schema theorem.
  • Advanced genetic algorithms, diploids, messy-chromozomes.
  • 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.

Progress assessment

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

Requirements for class accreditation are not defined.

Controlled instruction

Project is monitored

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