GRADUATE SCHOOL
M.SC. in Computer Engineering (With Thesis)
IE 509 | Course Introduction and Application Information
| Course Name |
Heuristics
|
|
Code
|
Semester
|
Theory
(hour/week) |
Application/Lab
(hour/week) |
Local Credits
|
ECTS
|
|
IE 509
|
Fall/Spring
|
3
|
0
|
3
|
7.5
|
| Prerequisites |
None
|
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| Course Language |
English
|
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| Course Type |
Elective
|
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| Course Level |
Second Cycle
|
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| Mode of Delivery | - | |||||
| Teaching Methods and Techniques of the Course | - | |||||
| Course Coordinator | ||||||
| Course Lecturer(s) | ||||||
| Assistant(s) | - | |||||
| Course Objectives | The purpose of this course is to fundamental concepts of heuristics in solving various optimization problems with emphasis on metaheuristics |
| Learning Outcomes |
The students who succeeded in this course;
|
| Course Description | This course introduces the concept of heuristics to the students who have already know about mathematical optimization. The topics include basic heuristic constructs (greedy, improvement, construction); meta heuristics such as simulated annealing, tabu search, genetic algorithms, ant algorithms and their hybrids. The basic material on the heuristic will be covered in regular lectures The students will be required to present a variety of application papers on different subjects related to the course. In addition, as a project assignment the students will design a heuristic, write a code of an appropriate algorithm for the problem and evaluate its performance. |
|
|
Core Courses | |
| Major Area Courses | ||
| Supportive Courses | ||
| Media and Management Skills Courses | ||
| Transferable Skill Courses |
WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES
| Week | Subjects | Related Preparation |
| 1 | Heuristics Basic Terminalogy | |
| 2 | Complexity | |
| 3 | Basic Search Procedures | |
| 4 | Simulated Annealing | |
| 5 | Tabu Search | |
| 6 | Genetic Algorithms and Evolutionary Search | |
| 7 | Genetic Algorithms and Evolutionary Search | |
| 8 | Midterm | |
| 9 | Particle Swarm Optimization | |
| 10 | Ant Colony Optimization | |
| 11 | Scatter Search | |
| 12 | Local Search | |
| 13 | Very Large Scale Neighborhood Search | |
| 14 | Presentations | |
| 15 | Presentations | |
| 16 | Review of the Semester |
| Course Notes/Textbooks | E.G. Talbi. Metaheuristics: From Design to Implementation. Wiley 2009. F. Glover, G. Kochenberger. Handbook of Metaheuristics. Springer 2003. T. González. Handbook of Approximation Algorithms and Metaheuristics. Chapman & Hall 2007. F. Glover, M. Laguna. Tabu Search. Kluwer, 1997. M. Dorigo and T. Stützle. Ant Colony Optimization. MIT Press, Cambridge, MA, 2004.
|
| Suggested Readings/Materials | Related Research Papers |
EVALUATION SYSTEM
| Semester Activities | Number | Weigthing |
| Participation | ||
| Laboratory / Application | ||
| Field Work | ||
| Quizzes / Studio Critiques | ||
| Portfolio | ||
| Homework / Assignments |
2
|
15
|
| Presentation / Jury |
1
|
15
|
| Project |
1
|
50
|
| Seminar / Workshop | ||
| Oral Exams | ||
| Midterm |
1
|
20
|
| Final Exam |
-
|
|
| Total |
| Weighting of Semester Activities on the Final Grade |
100
|
|
| Weighting of End-of-Semester Activities on the Final Grade | ||
| Total |
ECTS / WORKLOAD TABLE
| Semester Activities | Number | Duration (Hours) | Workload |
|---|---|---|---|
| Theoretical Course Hours (Including exam week: 16 x total hours) |
16
|
3
|
48
|
| Laboratory / Application Hours (Including exam week: '.16.' x total hours) |
16
|
0
|
|
| Study Hours Out of Class |
15
|
4
|
60
|
| Field Work |
0
|
||
| Quizzes / Studio Critiques |
0
|
||
| Portfolio |
0
|
||
| Homework / Assignments |
2
|
20
|
40
|
| Presentation / Jury |
1
|
17
|
17
|
| Project |
1
|
40
|
40
|
| Seminar / Workshop |
0
|
||
| Oral Exam |
0
|
||
| Midterms |
1
|
20
|
20
|
| Final Exam |
-
|
0
|
|
| Total |
225
|
COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP
|
#
|
Program Competencies/Outcomes |
* Contribution Level
|
||||
|
1
|
2
|
3
|
4
|
5
|
||
| 1 | Accesses information in breadth and depth by conducting scientific research in Computer Engineering; evaluates, interprets and applies information. |
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| 2 | Is well-informed about contemporary techniques and methods used in Computer Engineering and their limitations. | |||||
| 3 | Uses scientific methods to complete and apply information from uncertain, limited or incomplete data; can combine and use information from different disciplines. |
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| 4 | Is informed about new and upcoming applications in the field and learns them whenever necessary. | |||||
| 5 | Defines and formulates problems related to Computer Engineering, develops methods to solve them and uses progressive methods in solutions. |
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| 6 | Develops novel and/or original methods, designs complex systems or processes and develops progressive/alternative solutions in designs | |||||
| 7 | Designs and implements studies based on theory, experiments and modelling; analyses and resolves the complex problems that arise in this process. |
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| 8 | Can work effectively in interdisciplinary teams as well as teams of the same discipline, can lead such teams and can develop approaches for resolving complex situations; can work independently and takes responsibility. |
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| 9 | Engages in written and oral communication at least in Level B2 of the European Language Portfolio Global Scale. |
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| 10 | Communicates the process and the results of his/her studies in national and international venues systematically, clearly and in written or oral form. |
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| 11 | Is knowledgeable about the social, environmental, health, security and law implications of Computer Engineering applications, knows their project management and business applications, and is aware of their limitations in Computer Engineering applications. |
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| 12 | Highly regards scientific and ethical values in data collection, interpretation, communication and in every professional activity. |
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*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest