Učni načrt predmeta

Predmet:
Evolucijski algoritmi
Course:
Evolutionary Algorithms
Študijski program in stopnja /
Study programme and level		 Študijska smer /
Study field		 Letnik /
Academic year		 Semester /
Semester
Informacijske in komunikacijske tehnologije, 3. stopnja Inteligentni sistemi in robotika 1 1
Information and Communication Technologies, 3rd cycle Intelligent Systems and Robotics 1 1
Vrsta predmeta / Course type
Izbirni / Elective
Univerzitetna koda predmeta / University course code:
IKT3-622
Predavanja
Lectures		 Seminar
Seminar		 Vaje
Tutorial		 Klinične vaje
work		 Druge oblike
študija		 Samost. delo
Individ. work		 ECTS
15 15 15 105 5

*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:
Prerequisites:

Zaključen študij druge stopnje s področja informacijskih ali komunikacijskih tehnologij ali zaključen študij druge stopnje na drugih področjih z znanjem osnov s področja predmeta. Potrebna so tudi osnovna znanja matematike, računalništva in informatike.

Completed second-cycle studies in information or communication technologies or completed second-cycle studies in other fields with knowledge of fundamentals in the field of this course. Basic knowledge of mathematics, computer science and informatics is also requested.

Vsebina:
Content (Syllabus outline):

Uvod:
preiskovanje in optimizacija, optimizacijski problemi in njihove značilnosti, deterministična in stohastična optimizacija, optimizacijski algoritmi po zgledih iz narave, evolucijsko računanje, računska inteligenca

Osnove evolucijskih algoritmov:
motivacija, terminologija, zgradba in delovanje, vrste evolucijskih algoritmov, teoretično ozadje, prednosti in slabosti

Mehanizmi in tehnike:
uglaševanje parametrov algoritmov, obravnavanje omejitev, reševanje multimodalnih, dinamičnih in večkriterijskih optimizacijskih problemov, paralelizacija, hibridizacija

Vrednotenje in uporaba:
statistična analiza rezultatov, mere kakovosti rezultatov in računske učinkovitosti, razvoj evolucijskega algoritma za izbrani optimizacijski problem, primeri uporabe v znanosti, inženirstvu in poslovnem svetu

Sorodni algoritmi:
optimizacija z roji delcev, optimizacija s kolonijami mravelj, kulturni algoritmi, memetski algoritmi, umetni imunski sistemi

Introduction:
search and optimization, optimization problems and their characteristics, deterministic and stochastic optimization, nature-inspired optimization algorithms, evolutionary computation, computational intelligence

Foundations of evolutionary algorithms:
motivation, terminology, composition and functioning, types of evolutionary algorithms, theoretical background, advantages and disadvantages

Mechanisms and techniques:
algorithm parameter tuning, constraint handling, solving multimodal, dynamic and multiobjective optimization problems, parallelization, hybridization

Evaluation and applications:
statistical analysis of results, measures of effectiveness and efficiency, design of an evolutionary algorithm for a selected optimization problem, use cases from science, engineering and business

Related algorithms:
particle swarm optimization, ant colony optimization, cultural algorithms, memetic algorithms, artificial immune systems

Temeljna literatura in viri / Readings:

Izbrana poglavja iz naslednjih knjig: / Selected chapters from the following books:
- Th. Bäck, Ch. Foussette, and P. Krause. Contemporary Evolution Strategies. Springer, 2013. ISBN 978-3-
642-40136-7
- A. E. Eiben, and J. E. Smith. Introduction to Evolutionary Computing, 2nd edition. Springer, 2015. ISBN 978-3-662-44873-1
- Th. Jansen. Analyzing Evolutionary Algorithms. Springer, 2013. ISBN 978-3-642-17338-7
- G. Rozenberg, Th. Bäck, and J. N. Kok (Eds.). Handbook of Natural Computing. Springer, 2012. ISBN 978-
3-540-92909-3
- X. Yu, and M. Gen. Introduction to Evolutionary Algorithms. Springer, 2010. ISBN 978-1-84996-128-8

Cilji in kompetence:
Objectives and competences:

Cilji predmeta so:
- predstaviti osnove optimizacije in evolucijskega računanja,
- predstaviti gradnike in mehanizme evolucijskih algoritmov in njihove značilnosti,
- predstaviti metodologijo vrednotenja rezultatov in praktično uporabnost algoritmov,
- podati pregled sorodnih algoritmov.

Študenti, ki bodo uspešno končali ta predmet, bodo obvladali osnove evolucijskega računanja in bodo usposobljeni za uporabo evolucijskih algoritmov v reševanju zahtevnih optimizacijskih problemov in vrednotenje njihovih rezultatov.

The course objectives are to:
- introduce the basics of optimization and evolutionary computation,
- present the building-blocks and mechanisms of evolutionary algorithms and their characteristics,
- present the methodology of result evaluation and the algorithm practical potential,
- give an overview of the related algorithms.

The students who will successfully complete this course will master the basics of evolutionary computation and will be capable of applying evolutionary algorithms in solving demanding optimization problems and evaluating their results.

Predvideni študijski rezultati:
Intendeded learning outcomes:

Študenti bodo z uspešno opravljenimi obveznostmi tega predmeta pridobili:
- razumevanje konceptov optimizacije in evolucijskega računanja,
- obvladovanje tehničnih vidikov evolucijskih algoritmov,
- usposobljenost za njihov razvoj in uporabo v reševanju praktičnih problemov,
- usposobljenost za interpretacijo in vrednotenje njihovih rezultatov.

Students successfully completing this course will acquire:
- understanding the concepts of optimization and evolutionary computation,
- mastering technical aspects of evolutionary algorithms,
- ability to design the algorithms and apply them in practical problem solving,
- ability of to interpret and evaluate their results.

Metode poučevanja in učenja:
Learning and teaching methods:

Predavanja, seminar, konzultacije, samostojno delo

Lectures, seminar, consultations, individual work

Načini ocenjevanja:
Delež v % / Weight in %
Assesment:
Pisni ali ustni izpit
100 %
Written or oral exam
Reference nosilca / Lecturer's references:
1. VODOPIJA, Aljoša, TUŠAR, Tea, FILIPIČ, Bogdan. Characterization of constrained continuous multiobjective optimization problems: A performance space perspective. IEEE Transactions on Evolutionary Computation, 2024 (early access), doi: 10.1109/TEVC.2024.3366659.
2. VODOPIJA, Aljoša, TUŠAR, Tea, FILIPIČ, Bogdan. Characterization of constrained continuous multiobjective optimization problems: A feature space perspective. Information Sciences, 2022, vol. 607, pp. 244-262, doi: 10.1016/j.ins.2022.05.106.
3. VODOPIJA, Aljoša, STORK, Jörg, BARTZ-BEIELSTEIN, Thomas, FILIPIČ, Bogdan. Elevator group control as a constrained multiobjective optimization problem. Applied Soft Computing, 2022, vol. 115, pp. 108277-1-108277-14, doi: 10.1016/j.asoc.2021.108277.
4. KOBLAR, Valentin, FILIPIČ, Bogdan. Evolutionary design of a system for online surface roughness measurements. Mathematics, 2021, vol. 9, no. 16, pp. 1904-1-1904-18, doi: 10.3390/math9161904.
5. ZUPANČIČ, Jernej, FILIPIČ, Bogdan, GAMS, Matjaž. Genetic-programming-based multi-objective optimization of strategies for home energy-management systems. Energy, 2020, vol. 203, pp. 117769-1-117769-15, doi: 10.1016/j.energy.2020.117769.