Evolutionary Computing Optimization

Course Description

In everyday life we encounter different types of NP-hard optimization problems, whose approximate resolution enables efficient and cost-effective management of various processes. Within this skill, students will learn concepts of single- and multi-objective optimization, continuous and combinatorial optimization problems and with a subset of evolutionary computation algorithms that will be used to obtain satisfactory solutions. As part of this skill, students will learn about the genetic algorithms, ant colony optimization algorithm, particle swarm optimization algorithm, artificial immune algorithms and the algorithm of differential evolution, with examples of single- and multi-objective optimization of continuous and combinatorial problems. Parallelization of selected algorithms will be discussed and implemented.

Learning Outcomes

  1. define the concept of optimization problem
  2. identify evolutionary computation algorithms
  3. apply evolutionary computation algorithms on single-criterion optimization problems
  4. apply evolutionary computation algorithms on multi-criterion optimization problems
  5. construct parallel evolutionary computation algorithms
  6. assess the applicability of different evolutionary computation algorithms on some optimization problems

Forms of Teaching

Lectures

Exercises

Independent assignments

Week by Week Schedule

  1. Single objective optimization problem vs; Multi-objective optimization problem
  2. Direct Search Algorithms (the Hooke-Jeeves method); Gradient Methods (the steepest descent);
  3. Schemes for solution representation; Evolutionary operators (selection, mutation, recombination, etc;)
  4. Schemes for solution representation; Evolutionary operators (selection, mutation, recombination, etc;)
  5. Evolutionary algorithms for SOOP
  6. Evolutionary algorithms for SOOP
  7. Swarm-based algorithms for SOOP
  8. Midterm exam
  9. Swarm-based algorithms for SOOP
  10. Other evolutionary computation methods for SOOP
  11. Other evolutionary computation methods for SOOP
  12. Unconstrained problems; Handling of constraints
  13. Evolutionary computation and MOOP (paretto optimality and other approaches)
  14. Parallelization of evolutionary computation algorithms
  15. Final exam

Study Programmes

University graduate
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Literature

(.), Marko Čupić. Prirodom inspirirani optimizacijski algoritmi (online),
(.), El-Ghazali Talbi: Metaheuristics: From Design to Implementation, Wiley, 2009.,

Associate Lecturers

For students

General

ID 222578
  Winter semester
5 ECTS
L0 English Level
L1 e-Learning
45 Lectures
0 Seminar
0 Exercises
15 Laboratory exercises
0 Project laboratory

Grading System

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