Course details

Applied Evolutionary Algorithms

EVO Acad. year 2006/2007 Summer semester 5 credits

Current academic year

Theoretical foundation and practice of evolutionary computation. Genetic algorithms, evolution strategies, evolutionary programming and genetic programming 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. Classifiers and new paradigms of evolutionary algorithms. 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

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

Department

Department of Computer Systems (UPSY)

Subject specific learning outcomes and competences

  1. 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.
  2. Students known how to specify suitable genetic operators and control parameters of evolutionary process including the choice of the population size, rate 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. 

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.

Requirements for class accreditation are not defined.

Controlled instruction

Project is monitored

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