1	Course Title:	METAHEURISTIC ALGORITHMS
2	Course Code:	ELN6104
3	Type of Course:	Optional
4	Level of Course:	Third Cycle
5	Year of Study:	1
6	Semester:	2
7	ECTS Credits Allocated:	6
8	Theoretical (hour/week):	3
9	Practice (hour/week) :	0
10	Laboratory (hour/week) :	0
11	Prerequisites:
12	Recommended optional programme components:	None
13	Language:	Turkish
14	Mode of Delivery:	Face to face
15	Course Coordinator:	Prof. Dr. FAHRİ VATANSEVER
16	Course Lecturers:
17	Contactinformation of the Course Coordinator:	Adres: Elektrik-Elektronik Mühendisliği bölümü, No:311 Tel: (224) 294 09 05 Web: http://home.uludag.edu.tr/~fahriv E-posta:fahriv@uludag.edu.tr
18	Website:	http://home.uludag.edu.tr/~fahriv
19	Objective of the Course:	To present basics, mathematical background, types, application areas, programming techniques of evolutionary algorithms theoretically and practically, and to provide to be used these in real applications
20	Contribution of the Course to Professional Development	Effective use of metaheuristic algorithms in the professional area

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Learning Outcomes:

1 To provide adequate knowledge in evolutionary algorithms concept and ability to apply for modeling and solving engineering problems using theoretical and practical knowledge in this area; 2 To gain ability to detect, to descript, to formulize and to solve problems which belong to optimization area; 3 To gain ability to develop, choose and implement modern techniques and equipment which are needed for engineering applications, ability to use information technology effectively; 4 To gain ability to design, to realize experiments, to collect data, to analyze and to interpret results for investigation problems which belong to optimization area;

22	Course Content:

Week	Theoretical	Practical
1	Optimization
2	Test functions, objective functions, real-world problems and statistical analysis
3	Classification and coding
4	Evolutionary-based optimization algorithms
5	Evolutionary-based optimization algorithms
6	Nature-inspired optimization algorithms (swarm-based, physic/chemistry-based, bio-inspired, human-based etc.)
7	Nature-inspired optimization algorithms (swarm-based, physic/chemistry-based, bio-inspired, human-based etc.)
8	Nature-inspired optimization algorithms (swarm-based, physic/chemistry-based, bio-inspired, human-based etc.)
9	Midterm exam
10	Nature-inspired optimization algorithms (swarm-based, physic/chemistry-based, bio-inspired, human-based etc.)
11	Trajectory-based optimization algorithms
12	Other optimization algorithms
13	Up-to-date optimization algorithms
14	Up-to-date optimization algorithms

23	Textbooks, References and/or Other Materials:	1. Simon, D., Evolutionary Optimization Algorithms, Wiley, 2013. 2. Goldberg D E. Genetic Algorithms in Search, Optimization and Machine Learning., Addison-Wesley Longman Publishing, 1989. 3. Yang XS. Nature-Inspired Metaheuristic Algorithms. 2nd ed., Luniver Press, 2010.
24	Assesment

TERM LEARNING ACTIVITIES	NUMBER	PERCENT
Midterm Exam	1	40
Quiz	0	0
Homeworks, Performances	0	0
Final Exam	1	60
Total	2	100
Contribution of Term (Year) Learning Activities to Success Grade		40
Contribution of Final Exam to Success Grade		60
Total		100
Measurement and Evaluation Techniques Used in the Course	Midterm and final exams
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	6	84
Homeworks, Performances	0	0	0
Projects	0	0	0
Field Studies	0	0	0
Midtermexams	1	20	20
Others	0	0	0
Final Exams	1	34	34
Total WorkLoad			200
Total workload/ 30 hr			6
ECTS Credit of the Course			6

26	CONTRIBUTION OF LEARNING OUTCOMES TO PROGRAMME QUALIFICATIONS
PQ1 PQ2 PQ3 PQ4 PQ5 PQ6 LO1 5 0 0 0 0 0 LO2 0 5 0 0 0 0 LO3 0 0 0 5 0 0 LO4 0 0 0 0 5 0

LO: Learning Objectives

PQ: Program Qualifications

Contribution Level:

1 Very Low

2 Low

3 Medium

4 High

5 Very High