Course details

Applied Evolutionary Algorithms

EVO Acad. year 2007/2008 Summer semester 5 credits

Current academic year

Multiobjective optimization problems, standard approaches and stochastic evolutionary algorithms (EA), simulated annealing (SA). Evolution strategies (ES) and genetic algorithms (GA). Tools for fast prototyping. Representation of problems by graph models. Evolutionary algorithms in engineering applications namely in synthesis and physical design of digital circuits, artificial intelligence, signal processing, scheduling in multiprocessor systems and in business commercial applications.

Guarantor

Schwarz Josef, doc. Ing., CSc.

Language of instruction

Czech

Completion

Examination

Time span

  • 26 hrs lectures
  • 8 hrs pc labs
  • 18 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 of fast prototyping of evolutionary optimizer utilizing GA library and present design tools.

Learning objectives

Survey about actual optimization techniques and evolutionary algorithms for solution of complex, NP complete problems. To make students familiar with software tools for fast prototyping of evolutionary algorithms and learn how to solve typical complex tasks from engineering practice.

Prerequisite knowledge and skills

There are no prerequisites

Syllabus of lectures

  • Multiobjective optimization problems.
  • Standard approaches and stochastic evolutionary algorithms (EA).
  • Simulated annealing (SA).
  • Evolution strategies (ES).
  • Genetic algorithms (GA).
  • Tools for fast prototyping - GADesign, GAlib.
  • Representation of problems with graph models.
  • Optimization of digital circuit synthesis.
  • Optimization of circuit decomposition and allocation.
  • Evolvable hardware.
  • Optimization of digital filtering and data compression.
  • Scheduling in multiprocessor systems.
  • Utilizing of EA in commerce.

Progress assessment

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

Controlled instruction

Midterm and final test, one project.

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