Faculty of Information Technology, BUT

Course details

Applied Evolutionary Algorithms

EVA Acad. year 2005/2006 Summer semester 5 credits

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

Language of instruction

Czech

Completion

Examination (written)

Time span

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

Assessment points

50 exam, 20 half-term test, 30 projects

Department

Lecturer

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.

Prerequisites

Prerequisite kwnowledge 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.: Evolutionary algorithms. Publisher STU Bratislava, 2000, pp. 215, ISBN 80-227-1377-5.
  • Kvasnička V., a kol.: Introduction into theory of neural networks, 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.: Evolutionary algorithms. Publisher STU Bratislava, 2000, pp. 215, ISBN 80-227-1377-5.

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.

Syllabus - others, projects and individual work of students

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

Progress assessment

  • Mid-term written exam - 20 points.
  • Individual project - 30 points.
  • Final written examination - 50 points.
  • Passing boundary for ECTS assessment - 50 points.

Controlled instruction

Project is monitored

Exam prerequisites

Requirements for class accreditation are not defined.
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