Course detail

Evolutionary Computation

FIT-EVDAcad. year: 2021/2022

Evolutionary computation in the context of artificial intelligence and hard optimization problems. Single- and multi-objective optimization, dominance relation, Pareto front. Principles of genetic algorithms, evolutionary strategy, genetic programming and other evolutionary heuristics. Statistical evaluation, theoretical analysis of evolutionary algorithms. Advanced evolutionary algorithms based on probabilistic models. Parallel evolutionary algorithms. Multi-objective evolutionary algorithms. Rapid prototyping of evolutionary algorithms.


Doctoral state exam - topics:

  1. Problem encoding, genotype, phenotype, fitness function.
  2. Genetic algorithms, schema theory.
  3. Evolution strategies.
  4. Genetic programming and symbolic regression.
  5. Estimation distribution algorithms.
  6. Simulated annealing
  7. Multi-objective evolutionary optimization.
  8. Parallel evolutionary algorithms.
  9. Differential evolution, SOMA.
  10. Statistical analysis of experiments.

Language of instruction

Czech

Mode of study

Not applicable.

Guarantor

prof. Ing. Lukáš Sekanina, Ph.D.

Department

Department of Computer Systems (UPSY)

Learning outcomes of the course unit

Skills and approaches required for solving hard optimization problems using evolutionary algorithms.
Deeper understanding of the optimization problem and its solution in computer engineering.

Prerequisites

Not applicable.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

Submission of the project on time, exam.

Course curriculum

Not applicable.

Work placements

Not applicable.

Aims

To acquaint students with modern evolutionary algorithms developed for solving hard optimization and design problems.

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

During the course, it is necessary to submit the project and pass the exam. Teaching is performed as lectures or controlled self-study; the missed classes need to be replaced by self-study.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Not applicable.

Recommended reading

Bentley, P.: Evolutionary Design by Computers. Morgan Kaufmann, 1999, ISBN 978-1558606050.
Brabazon, A., O'Neill, M., McGarraghy, S.: Natural Computing Algorithms. Springer, 2015, ISBN 978-3-662-43630-1.
Doerr, B. Neumann F. (eds.): Theory of Evolutionary Computation. Springer, 2020, ISBN 978-3-030-29413-7
Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. 2nd ed. Springer, 2015, ISBN 978-3-662-44873-1.
Kvasnička V., Pospíchal J., Tiňo P.: Evoluční algoritmy. Vydavatelství STU Bratislava, 2000, str. 215, ISBN 80-227-1377-5.

Classification of course in study plans

  • Programme DIT Doctoral 0 year of study, summer semester, compulsory-optional
  • Programme DIT Doctoral 0 year of study, summer semester, compulsory-optional

  • Programme CSE-PHD-4 Doctoral

    branch DVI4 , 0 year of study, summer semester, elective

  • Programme CSE-PHD-4 Doctoral

    branch DVI4 , 0 year of study, summer semester, elective

  • Programme DIT-EN Doctoral 0 year of study, summer semester, compulsory-optional
  • Programme DIT-EN Doctoral 0 year of study, summer semester, compulsory-optional

  • Programme CSE-PHD-4 Doctoral

    branch DVI4 , 0 year of study, summer semester, elective

  • Programme CSE-PHD-4 Doctoral

    branch DVI4 , 0 year of study, summer semester, elective

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

prof. Ing. Lukáš Sekanina, Ph.D.

Syllabus

  1. Introduction to evolutionary computation.
  2. Genetic algorithms, schema theory.
  3. Statistical analysis of experiments.
  4. Typical optimization problems.
  5. Advanced techniques in genetic algorithms.
  6. Theoretical analysis of evolutionary algorithms.
  7. Multi-objective evolutionary optimization.
  8. Evolution strategies.
  9. Genetic programming and symbolic regression.
  10. Parallel evolutionary algorithms.
  11. Estimation distribution algorithms.
  12. Simulated annealing, differential evolution, SOMA and other relevant algorithms.
  13. Recent trends.

Guided consultation in combined form of studies

26 hod., optionally

Teacher / Lecturer

prof. Ing. Lukáš Sekanina, Ph.D.