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Course Information
Course Unit Title : Heuristics Optimization
Course Unit Code : 01END5103
Type of Course Unit : Optional
Level of Course Unit : Second Cycle
Year of Study : Preb
Semester : 255.Semester
Number of ECTS Credits Allocated : 6,00
Name of Lecturer(s) :
Course Assistants :
Learning Outcomes of The Course Unit : 1) Prepare algorithm
2) Draws flow chart symbols and flow charts
3) Explains and applies basic concepts in programming
4) Exhibits the skills about teaching algorithm concept
Mode of Delivery : Face-To-Face
Prerequisities and Co-requisities Courses : Unavailable
Recommended Optional Programme Components : Unavailable
Course Contents : Basic concepts in Algorithm, Usage and techniques to develop Algorithm, Transitions from operations to programming
Languages of Instruction : Turkish
Course Goals : To teach basic concepts about Algorithms, Flow Charts and Entering Programming Languages
Course Aims : To teach basic concepts about Algorithms, Flow Charts and Entering Programming Languages
WorkPlacement   Not Available
Recommended or Required Reading
Textbook : Lecture Notes
Additional Resources : - Bakır, M. A., Altunkaynak, B., Tamsayılı Programlama, Teori, Modeller ve Algoritmalar, Nobel Yayınevi, 2003.
Material Sharing
Documents :
Assignments :
Exams :
Additional Material :
Planned Learning Activities and Teaching Methods
Lectures, Practical Courses, Presentation, Seminar, Project, Laboratory Applications (if necessary)
ECTS / Table Of Workload (Number of ECTS credits allocated)
Student workload surveys utilized to determine ECTS credits.
Activity :
Number Duration Total  
Course Duration (Excluding Exam Week) :
14 3 42  
Time Of Studying Out Of Class :
14 4 56  
Homeworks :
6 3 18  
Presentation :
0 0 0  
Project :
1 10 10  
Lab Study :
0 0 0  
Field Study :
0 0 0  
Visas :
1 20 20  
Finals :
1 30 30  
Workload Hour (30) :
30  
Total Work Charge / Hour :
176  
Course's ECTS Credit :
6      
Assessment Methods and Criteria
Studies During Halfterm :
Number Co-Effient
Visa :
1 30
Quiz :
0 0
Homework :
0 0
Attendance :
0 0
Application :
0 0
Lab :
0 0
Project :
1 30
Workshop :
0 0
Seminary :
0 0
Field study :
0 0
   
TOTAL :
60
The ratio of the term to success :
60
The ratio of final to success :
40
TOTAL :
100
Weekly Detailed Course Content
Week Topics  
1 Introduction
 
2 Greedy Algorithms
 
3 Greedy Algorithms
 
4 Genetic Algorithms
 
5 Genetic Algorithms
 
6 Simulated Annealing
 
7 Simulated Annealing
 
8 Tabu Search
 
9 Tabu Search
 
10 Ant Colony Optimization
 
11 Ant Colony Optimization
 
12 New Meta-Heuristics Methods
 
13 New Meta-Heuristics Methods
 
14 Heuristic Programming