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Solving Optimization Problems Using Evolutionary Computation Algorithms in Java
Abbreviation: Load: 30(L) + 0(E) + 0(LE) + 0(CE)
Lecturers in charge:
Lecturers: dr. sc. Marko Čupić ( Lectures )
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, and examples will be given in the Java programming language.
Lecture languages: - - -
Compulsory literature:
1. Prirodom inspirirani optimizacijski algoritmi;Marko Čupić;skripta 2008.
3. Differential Evolution. A Practical Approach to Global Optimization; Kenneth V.Price, Rainer M. Storn, Jouni A. Lampinen; Springer 2005
Recommended literature:
2. Genetic Algorithms and Genetic Programming. Modern Concepts and Practical Applications;Michael Affenzeller, Stefan Wagner, Stephan Winkler, Andreas Beham;CRC Press 2009
4. Multi-Objective Optimization using Evolutionary Algorithms; Kalyanmoy Deb; Wiley 2009
5. Ant Colony Optimization;Marco Dorigo, Thomas Stützle;MIT Press 2004
Legend
L - Lectures
E - Exercises
LE - Laboratory exercises
CE - Project laboratory
* - Not graded
Copyright (c) 2006. Ministarstva znanosti, obrazovanja i športa. Sva prava zadržana.
Programska podrška (c) 2006. Fakultet elektrotehnike i računarstva.
Oblikovanje(c) 2006. Listopad Web Studio.
Posljednja izmjena 2012-12-19