Course details

Applied Evolutionary Algorithms

EVO Acad. year 2017/2018 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

Bidlo Michal, doc. Ing., Ph.D. (DCSY)

Language of instruction

Czech

Completion

Examination (written)

Time span

  • 26 hrs lectures
  • 8 hrs pc labs
  • 18 hrs projects

Assessment points

  • 60 pts final exam (written part)
  • 18 pts labs
  • 22 pts 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

  • Evolutionary algorithms, theoretical foundation, basic distribution (GA, EP,GP, ES).
  • Genetic algorithms (GA), schemata theory.
  • Genetic algorithms using diploids and messy-chromosomes. Specific crossing.
  • Representative combinatorial optimization problems.
  • Evolutionary programming, Hill climbing algorithm, Simulated annealing. 
  • Genetic programming.
  • Advanced estimation distribution algorithms (EDA).
  • Variants of EDA algorithms, UMDA, BMDA and BOA.
  • Multimodal and multi-criterial optimization.
  • Dynamic optimization problems.
  • New evolutionary paradigm: immune systems,  differential evolution, SOMA.
  • Differential evolution. Particle swarm model. 
  • Engineering tasks and evolutionary algorithms.

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.

Course inclusion in study plans

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