Učni načrt predmeta

Predmet:
Stohastične optimizacijske metode
Course:
Stochastic Optimization Methods
Študijski program in stopnja /
Study programme and level
Študijska smer /
Study field
Letnik /
Academic year
Semester /
Semester
Informacijske in komunikacijske tehnologije, 2. stopnja Inteligentni sistemi in robotika 1 2
Information and Communication Technologies, 2nd cycle Intelligent Sytems and Robotics 1 2
Vrsta predmeta / Course type
Izbirni / Elective
Univerzitetna koda predmeta / University course code:
IKT2-614
Predavanja
Lectures
Seminar
Seminar
Vaje
Tutorial
Klinične vaje
work
Druge oblike
študija
Samost. delo
Individ. work
ECTS
30 30 30 210 10

*Navedena porazdelitev ur velja, če je vpisanih vsaj 15 študentov. Drugače se obseg izvedbe kontaktnih ur sorazmerno zmanjša in prenese v samostojno delo. / This distribution of hours is valid if at least 15 students are enrolled. Otherwise the contact hours are linearly reduced and transfered to individual work.

Nosilec predmeta / Course leader:
prof. dr. Bogdan Filipič
Sodelavci / Lecturers:
Jeziki / Languages:
Predavanja / Lectures:
slovenščina, angleščina / Slovenian, English
Vaje / Tutorial:
Pogoji za vključitev v delo oz. za opravljanje študijskih obveznosti:
Prerequisits:
Vsebina:
Content (Syllabus outline):
Temeljna literatura in viri / Readings:
Cilji in kompetence:
Objectives and competences:
Predvideni študijski rezultati:
Intendeded learning outcomes:
Metode poučevanja in učenja:
Learning and teaching methods:
Načini ocenjevanja:
Delež v % / Weight in %
Assesment:
Seminarska naloga
50 %
Seminar work
Pisni ali ustni izpit
50 %
Written or oral exam
Reference nosilca / Lecturer's references:
1. T. Tušar, and B. Filipič. “Visualization of Pareto front approximations in evolutionary multiobjective optimization: A critical review and the prosection method.” IEEE Transactions on Evolutionary Computation, vol. 19, no. 2, pp. 225-245, 2015.
2. M. Mlakar, D. Petelin, T. Tušar, and B. Filipič. “GP-DEMO: Differential evolution for multiobjective optimization based on Gaussian process models.” European Journal of Operational Research, vol. 243, no. 2, pp. 347-361, 2015.
3. E. Dovgan, M. Javorski, T. Tušar, M. Gams, and B. Filipič. “Discovering driving strategies with a multiobjective optimization algorithm.” Applied Soft Computing, vol. 16, no. 1, pp. 50-62, 2014.
4. M. Depolli, R. Trobec, and B. Filipič. “Asynchronous master-slave parallelization of differential evolution for multiobjective optimization.” Evolutionary Computation, vol. 21, no. 2, pp. 261-291, 2013.
5. P. Korošec, J. Šilc, and B. Filipič. “The differential ant-stigmergy algorithm.” Information Sciences, vol. 192, no. 1, pp. 82-97, 2012.