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:
17	Contactinformation of the Course Coordinator:	nursel@uludag.edu.tr +90 224 2942083 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

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	Simulated Annealing algorithm
3	Simulated Annealing algorithm, application examples
4	Tabu Search algorithm
5	Tabu Search algorithm, application examples
6	Genetic Algorithms, presentation of homework 1
7	Genetic Algorithms
8	Ant Colony Algorithms
9	Ant Colony Algorithms
10	Application examples, presentation of homework 2, Midterm Exam
11	Differential Evolution Algorithm
12	Artificial Immune System
13	Application examples, Presentation of homework 3
14	Oral presentation of projects

23	Textbooks, References and/or Other Materials:	N. Öztürk, “Sezgisel Algoritmalar Course Notes”. D.E. Goldberg, “Genetic Algorithms in Search, Optimization and Machine Learning”. Articles
24	Assesment

TERM LEARNING ACTIVITIES	NUMBER	PERCENT
Midterm Exam	1	20
Quiz	0	0
Homeworks, Performances	4	50
Final Exam	1	30
Total	6	100
Contribution of Term (Year) Learning Activities to Success Grade		70
Contribution of Final Exam to Success Grade		30
Total		100
Measurement and Evaluation Techniques Used in the Course
Information

25

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	4	3	12
Projects	1	25	25
Field Studies	0	0	0
Midtermexams	1	2,5	2,5
Others	0	0	0
Final Exams	1	3,5	3,5
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 PQ10 PQ11 PQ12 PQ13 LO1 0 5 0 0 0 0 0 5 0 0 0 0 0 LO2 0 5 0 0 0 0 0 5 0 0 0 0 0 LO3 0 0 5 0 5 4 0 5 0 0 4 4 0

LO: Learning Objectives

PQ: Program Qualifications

Contribution Level:

1 Very Low

2 Low

3 Medium

4 High

5 Very High