Course detail

Applied Evolutionary Algorithms

FIT-EVOAcad. year: 2017/2018

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.

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.

Recommended optional programme components

Not applicable.

Recommended or required reading

  • Kvasnička V., Pospíchal J., Tiňo P.: Evoluční algoritmy. Vydavatelství STU Bratislava, 2000, str. 215, ISBN 80-227-1377-5
  • Kvasnička V., a kol.: Úvod do teorie neuronových sítí, Iris 1997, ISBN 80-88778-30-1.

  • 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.: Evoluční algoritmy. Vydavatelství STU Bratislava, 2000, str. 215, ISBN 80-227-1377-5.

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.

Language of instruction

Czech

Work placements

Not applicable.

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:
    • Implementation of a given application from the field of evolutionary computation or
    • study of a given paper, presentation of main ideas.
    By agreement there is a possibility to include solution of the project from other course (e.g. BIN) to EVO if its topic belongs to evolutionary computation.


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.

Classification of course in study plans

  • Programme IT-MGR-2 Master's

    branch MBI , any year of study, summer semester, 5 credits, compulsory-optional
    branch MPV , any year of study, summer semester, 5 credits, compulsory-optional
    branch MGM , any year of study, summer semester, 5 credits, elective
    branch MSK , any year of study, summer semester, 5 credits, elective
    branch MIS , any year of study, summer semester, 5 credits, elective
    branch MBS , any year of study, summer semester, 5 credits, elective
    branch MIN , any year of study, summer semester, 5 credits, elective
    branch MMI , any year of study, summer semester, 5 credits, elective
    branch MMM , any year of study, summer semester, 5 credits, elective

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

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 hours, optionally

Teacher / Lecturer

Project

18 hours, optionally

Teacher / Lecturer

eLearning