GAZI UNIVERSITY INFORMATION PACKAGE - 2019 ACADEMIC YEAR

COURSE DESCRIPTION
INTELLIGENT OPTIMIZATION TECHNIQUES/5031305
Course Title: INTELLIGENT OPTIMIZATION TECHNIQUES
Credits 3 ECTS 8
Semester 1 Compulsory/Elective Elective
COURSE INFO
 -- LANGUAGE OF INSTRUCTION
  Turkish
 -- NAME OF LECTURER(S)
  Prof. Dr. M. Ali Akcayol
 -- WEB SITE(S) OF LECTURER(S)
  http://w3.gazi.edu.tr/web/akcayol/
 -- EMAIL(S) OF LECTURER(S)
  akcayol@gazi.edu.tr
 -- LEARNING OUTCOMES OF THE COURSE UNIT
Learning applying intelligent optimization techniques to complex engineering problems.
Learning ant algorithms
Learning simulated annealing
Learning tabu search
Learning genetic algorithms
Learning artificial neural networks



 -- MODE OF DELIVERY
  The mode of delivery of this course is face to face
 -- PREREQUISITES AND CO-REQUISITES
  There is no prerequisite or co-requisite for this course
 -- RECOMMENDED OPTIONAL PROGRAMME COMPONENTS
  There is no recommended optional programme component for this course
 --COURSE CONTENT
1. Week  Introduction to optimization
2. Week  Linear programming
3. Week  Classical search methods
4. Week  Simulated annealing
5. Week  Tabu search
6. Week  Ant algorithms
7. Week  Genetic algorithms
8. Week  Fuzzy logic
9. Week  Artificial intelligence
10. Week  Midterm
11. Week  Project
12. Week  Project
13. Week  Project
14. Week  Project
15. Week  Project
16. Week  Project
 -- RECOMMENDED OR REQUIRED READING
  How to Solve It: Modern Heuristics 2nd ed. Revised and Extended, Michalewicz Zbigniew, Fogel David B., Springer-Verlag, 2004 -Intelligent Optimization Techniques, Pham, D.T., Karaboga, D., Springer Verlag, 1999 -Elements of Artificial Neural Networks, Kishan Mehrotra, Chilukuri K. Mohan and Sanjay Ranka, MIT Press, 1996
 -- PLANNED LEARNING ACTIVITIES AND TEACHING METHODS
  Lecture, Question & Answer, Demonstration, Practise
 -- WORK PLACEMENT(S)
  -
 -- ASSESSMENT METHODS AND CRITERIA
 
Quantity
Percentage
 Mid-terms
1
35
 Assignment
6
25
 Exercises
0
0
 Projects
0
0
 Practice
0
0
 Quiz
0
0
 Contribution of In-term Studies to Overall Grade  
60
 Contribution of Final Examination to Overall Grade  
40
 -- WORKLOAD
 Efficiency  Total Week Count  Weekly Duration (in hour)  Total Workload in Semester
 Theoretical Study Hours of Course Per Week
14
3
42
 Practising Hours of Course Per Week
0
0
0
 Reading
14
1
14
 Searching in Internet and Library
14
1
14
 Designing and Applying Materials
0
0
0
 Preparing Reports
8
5
40
 Preparing Presentation
2
8
16
 Presentation
2
1
2
 Mid-Term and Studying for Mid-Term
1
22
22
 Final and Studying for Final
1
38
38
 Other
0
0
0
 TOTAL WORKLOAD: 
188
 TOTAL WORKLOAD / 25: 
7.52
 ECTS: 
8
 -- COURSE'S CONTRIBUTION TO PROGRAM
NO
PROGRAM LEARNING OUTCOMES
1
2
3
4
5
1Improves and deepens the field knowledge at an expert level based on undergraduate proficiency.X
2Comprehends the interactions between the computer science and other related disciplines.X
3Uses expert level theoretical and practical knowledge acquired in the computer science field.X
4Creates new knowledge by integrating the computer science knowledge and the knowledge from related disciplines.X
5Defines a problem in the computer science field.X
6Analyses the problems in the computer science field by using scientific research methods.X
7Proposes solutions to the problems in the computer science field.X
8Solves problems in the computer science field.X
9Evaluates the results within the perspectives of quality processes.X
10Develops new approaches and methods by taking responsibility in complex situations in the application stages.X