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Lecture type	Total
Lectures	30

* Load is given in academic hour (1 academic hour = 45 minutes)

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, and examples will be given in the Java programming language.

1. Prirodom inspirirani optimizacijski algoritmi; Marko Čupić; 2008
2. Genetic Algorithms and Genetic Programming. Modern Concepts and Practical Applications; Michael Affenzeller, Stefan Wagner, Stephan Winkler, Andreas Beham; CRC Press; 2009; ISBN: 978-1584886297
3. Differential Evolution. A Practical Approach to Global Optimization; Kenneth V.Price, Rainer M. Storn, Jouni A. Lampinen; Springer; 2005; ISBN: 978-3540209508
4. Multi-Objective Optimization using Evolutionary Algorithms; Kalyanmoy Deb; Wiley; 2009; ISBN: 978-0470743614
5. Ant Colony Optimization; Marco Dorigo, Thomas Stützle; MIT Press; 2004; ISBN: 978-0262042192

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