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OFTF
OF FACULTY of TECHNOLOGY / DEPARTMENT of ELECTRONICS and COMMUNICATION ENGINEERING /
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EHM3012Heuristic optimization2+0+0ECTS:4
Year / SemesterSpring Semester
Level of CourseFirst Cycle
Status Elective
DepartmentDEPARTMENT of ELECTRONICS and COMMUNICATION ENGINEERING
Prerequisites and co-requisitesNone
Mode of Delivery
Contact Hours14 weeks - 2 hours of lectures per week
LecturerDr. Öğr. Üyesi Eyüp GEDİKLİ
Co-Lecturerv
Language of instructionTurkish
Professional practise ( internship ) None
 
The aim of the course:
In this course, basic information about heuristic optimization algorithms will be given.
 
Learning OutcomesCTPOTOA
Upon successful completion of the course, the students will be able to :
LO - 1 : Gains general information about optimization. 1,2,5
LO - 2 : Learns heuristic optimization problems at a basic level. 1,2,5
LO - 3 : Basically learns and applies heuristic optimization algorithms.1,2,5
CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), LO : Learning Outcome

 
Contents of the Course
What is Optimizing? What is heuristic optimization? A* search (A star), Beam search, Hill climbing algorithm, Best first search, Greedy best first search, Simulated Annealing ) algorithm, backtracking, general purpose heuristic optimization algorithms, biology-based, physics-based, swarm-based, social-based, music-based and chemistry-based optimization algorithms will be discussed.
 
Course Syllabus
 WeekSubjectRelated Notes / Files
 Week 1What is Optimizing? What is heuristic optimization?
 Week 2A* search (A star)
 Week 3Beam search
 Week 4Hill climbing algorithm
 Week 5 Best first search
 Week 6Greedy best first search
 Week 7Simulated Annealing algorithm,
 Week 8backtracking
 Week 9Midterm exam
 Week 10Learning and applying the selected algorithm from the current heuristic optimization algorithms
 Week 11Learning and applying the selected algorithm from the current heuristic optimization algorithms
 Week 12Learning and applying the selected algorithm from the current heuristic optimization algorithms
 Week 13Learning and applying the selected algorithm from the current heuristic optimization algorithms
 Week 14Learning and applying the selected algorithm from the current heuristic optimization algorithms
 Week 15Learning and applying the selected algorithm from the current heuristic optimization algorithms
 Week 16Final exam
 
Textbook / Material
 
Recommended Reading
 
Method of Assessment
Type of assessmentWeek NoDate

Duration (hours)Weight (%)
Mid-term exam 9 1.04.2022 2 50
End-of-term exam 16 1.05.2022 2 50
 
Student Work Load and its Distribution
Type of workDuration (hours pw)

No of weeks / Number of activity

Hours in total per term
Yüz yüze eğitim 2 14 28
Sınıf dışı çalışma 3 14 42
Arasınav için hazırlık 4 1 4
Arasınav 1 2 2
Dönem sonu sınavı için hazırlık 4 2 8
Dönem sonu sınavı 1 2 2
Total work load86