1	Course Title:	HEURISTIC ALGORITHMS
2	Course Code:	END5123
3	Type of Course:	Optional
4	Level of Course:	Second Cycle
5	Year of Study:	1
6	Semester:	1
7	ECTS Credits Allocated:	7,5
8	Theoretical (hour/week):	3
9	Practice (hour/week) :	0
10	Laboratory (hour/week) :	0
11	Prerequisites:	None
12	Recommended optional programme components:	None
13	Language:	Turkish
14	Mode of Delivery:	Face to face
15	Course Coordinator:	Prof. Dr. NURSEL ÖZTÜRK
16	Course Lecturers:	Doç. Dr. İLKER KÜÇÜKOĞLU Dr. Öğr. Üyesi SEVAL ENE YALÇIN
17	Contactinformation of the Course Coordinator:	nursel@uludag.edu.tr +90 224 2942083 Bursa Uludağ Üniversitesi Endüstri Mühendisliği Bölümü
18	Website:
19	Objective of the Course:	The objective of this course is to provide students the knowledge of Heuristic Algorithms with engineering applications.
20	Contribution of the Course to Professional Development	The contribution of the course to the professional development is to introduce the knowledge and applications about heuristic algorithms, and to provide ability to apply the learned heuristic algorithms.

21

Learning Outcomes:

1 Will be able to have knowledge and understanding of heuristic algorithms; 2 Will be able to solve the engineering problems using the heuristic algorithms.; 3 Will be able to present a heuristic algorithm project;

22	Course Content:

Week	Theoretical	Practical
1	Introduction to heuristic algorithms
2	Local search methods
3	Simulated annealing algorithm
4	Tabu search algorithm
5	Tabu search algorithm, application examples
6	Genetic algorithms
7	Genetic algorithms
8	Differential evolution algorithm
9	Genetic algorithm and Differential evolution algorithm application examples
10	Particle swarm optimization and application examples
11	Ant colony algorithms
12	Adaptation of heuristic algorithms to constrained optimization problems
13	Parameter tuning and performance analyses for heuristic algorithms
14	Hybrid and parallel meta-heuristic algorithms

23	Textbooks, References and/or Other Materials:	Modern Sezgisel Teknikler ve Uygulamaları, Tunçhan Cura, 2008, Papatya Yayıncılık. Yapay Zeka Optimizasyon Algoritmaları, Derviş Karaboğa, 2014, Nobel Yayın. Handbook of Metaheuristics, Michel Gendreau and Jean-Yves Potvin, Springer. Metaheuristics From Design to Implementation, El-Ghazali Talbi, 2009, Wiley. Search and Optimization by Metaheuristics – Techniques and Algorithms Inspired by Nature, Ke-Lin Du and M.N.S Swamy, 2016, Birkauser. Differential evolution a practical approach to global optimization, Price, K.V.,Storn, R.M., Lampinen, J.A., 2005, Springer-Verlag, Berlin Heidelberg. Multidimensional particle swarm optimization for machine learning and pattern recognition. Kiranyaz, S., Ince, T., Gabbouj, M. 2014. Springer-Verlag, New York, USA.
24	Assesment

TERM LEARNING ACTIVITIES	NUMBER	PERCENT
Midterm Exam	0	0
Quiz	0	0
Homeworks, Performances	3	50
Final Exam	1	50
Total	4	100
Contribution of Term (Year) Learning Activities to Success Grade		50
Contribution of Final Exam to Success Grade		50
Total		100
Measurement and Evaluation Techniques Used in the Course	Homework, Project, Final Exam
Information

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ECTS / WORK LOAD TABLE

Activites	NUMBER	TIME [Hour]	Total WorkLoad [Hour]
Theoretical	14	3	42
Practicals/Labs	0	0	0
Self Study and Preparation	14	10	140
Homeworks, Performances	3	8	16
Projects	1	25	25
Field Studies	0	0	0
Midtermexams	0	0	0
Others	0	0	0
Final Exams	1	2	2
Total WorkLoad			225
Total workload/ 30 hr			7,5
ECTS Credit of the Course			7,5

26	CONTRIBUTION OF LEARNING OUTCOMES TO PROGRAMME QUALIFICATIONS
PQ1 PQ2 PQ3 PQ4 PQ5 PQ6 PQ7 PQ8 PQ9 LO1 0 5 0 0 0 0 0 5 0 LO2 0 5 0 0 0 0 0 5 0 LO3 0 0 5 0 5 4 0 5 0

LO: Learning Objectives

PQ: Program Qualifications

Contribution Level:

1 Very Low

2 Low

3 Medium

4 High

5 Very High