Course detail

Applied Evolutionary Algorithms

FIT-EVOAcad. year: 2014/2015

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.

Language of instruction

Czech

Number of ECTS credits

5

Mode of study

Not applicable.

Guarantor

doc. Ing. Josef Schwarz, CSc.

Department

Department of Computer Systems (UPSY)

Learning outcomes of the course unit

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.

Prerequisites

There are no prerequisites

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

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

Course curriculum

    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.

     
    Syllabus of laboratory exercises:

    • Simple design of an optimizer with GADesign system.
    • Utilizing of GA libraries like GAlib.
    • Genetic programming in Java.
    • Illustration of the program BMDA.

     
    Syllabus - others, projects and individual work of students:

    • Program for the optimization of given problem on the base of evolutionary computation.

Work placements

Not applicable.

Aims

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.

Specification of controlled education, way of implementation and compensation for absences

Midterm and final test, one project.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Not applicable.

Recommended reading

Not applicable.

Classification of course in study plans

  • Programme IT-MSC-2 Master's

    branch MBI , 0 year of study, summer semester, compulsory-optional
    branch MBS , 0 year of study, summer semester, elective
    branch MIN , 0 year of study, summer semester, elective
    branch MIS , 0 year of study, summer semester, elective
    branch MMI , 0 year of study, summer semester, elective
    branch MMM , 0 year of study, summer semester, elective
    branch MPV , 2 year of study, summer semester, compulsory-optional
    branch MSK , 0 year of study, summer semester, elective
    branch MGM , 0 year of study, summer semester, elective

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

doc. Ing. Josef Schwarz, CSc.

Syllabus

  • 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.

 

Exercise in computer lab

8 hod., optionally

Teacher / Lecturer

doc. Ing. Josef Schwarz, CSc.

Project

18 hod., optionally

Teacher / Lecturer

doc. Ing. Josef Schwarz, CSc.