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Ph.D. In Electrical-Electronics Engineering
IE 530 | Course Introduction and Application Information
| Course Name |
Evolutionary Algorithms
|
|
Code
|
Semester
|
Theory
(hour/week) |
Application/Lab
(hour/week) |
Local Credits
|
ECTS
|
|
IE 530
|
Fall/Spring
|
3
|
0
|
3
|
7.5
|
| Prerequisites |
None
|
|||||
| Course Language |
English
|
|||||
| Course Type |
Elective
|
|||||
| Course Level |
Second Cycle
|
|||||
| Mode of Delivery | - | |||||
| Teaching Methods and Techniques of the Course | - | |||||
| National Occupation Classification | - | |||||
| Course Coordinator | ||||||
| Course Lecturer(s) | ||||||
| Assistant(s) | - | |||||
| Course Objectives | The purpose of this course is to introduce the main concepts and applications in the field of evolutionary computation, with emphasis on evolutionary algorithms and swarm intelligence based problem-solving techniques. It will also provide students with practical experience with evolutionary algorithms for search and optimization.. |
| Learning Outcomes |
The students who succeeded in this course;
|
| Course Description | This course teaches basic evolutionary algorithms to the students and helps them gain experience in applying some of them. Among the topics of the course are, theoretical foundations of evolutionary algorithms, genetic algorithms, evolutionary operators, particle swarm optimization, and differential evolution algorithm. |
|
|
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 | Learning Outcome |
| 1 | Introduction to Evolutionary algorithms | ||
| 2 | Genetic Algorithms – basics | ||
| 3 | Genetic Algorithms - operators & selection schemes | ||
| 4 | Genetic Algorithms - operators & selection schemes | ||
| 5 | Differential evolution algorithm | ||
| 6 | Particle swarm optimization | ||
| 7 | Midterm | ||
| 8 | New generation bio-inspired algorithms | ||
| 9 | Evolutionary algorithm applications for continuous spaces | ||
| 10 | Evolutionary algorithm applications for binary spaces | ||
| 11 | Evolutionary algorithm applications for combinatorial spaces | ||
| 12 | Memetic algorithms | ||
| 13 | Memetic algorithms | ||
| 14 | Learning schemes & self-adaptation | ||
| 15 | Review of final and presentations | ||
| 16 | Review of the Semester and presentations |
| Course Notes/Textbooks | Günther Zäpfel, Roland Braune, Michael Bögl (2010). Metaheuristic Search Concepts A Tutorial with Applications to Production and Logistics. Springer. Mitsuo Gen, Runwei Cheng, (2000). Genetic Algorithms and Engineering Optimization. Wiley. |
| Suggested Readings/Materials | Zelinka, I. (2015). A survey on evolutionary algorithms dynamics and its complexity–Mutual relations, past, present and future. Swarm and Evolutionary Computation, 25, 2-14. |
EVALUATION SYSTEM
| Semester Activities | Number | Weigthing |
| Participation | ||
| Laboratory / Application | ||
| Field Work | ||
| Quizzes / Studio Critiques | ||
| Portfolio | ||
| Homework / Assignments | ||
| Presentation / Jury |
1
|
15
|
| Project |
1
|
25
|
| Seminar / Workshop | ||
| Oral Exams | ||
| Midterm |
1
|
20
|
| Final Exam |
1
|
40
|
| Total |
| Weighting of Semester Activities on the Final Grade |
3
|
60
|
| Weighting of End-of-Semester Activities on the Final Grade |
1
|
40
|
| 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 |
14
|
5
|
70
|
| Field Work |
0
|
||
| Quizzes / Studio Critiques |
0
|
||
| Portfolio |
0
|
||
| Homework / Assignments |
0
|
||
| Presentation / Jury |
1
|
18
|
18
|
| Project |
1
|
34
|
34
|
| Seminar / Workshop |
0
|
||
| Oral Exam |
0
|
||
| Midterms |
1
|
25
|
25
|
| Final Exam |
1
|
30
|
30
|
| Total |
225
|
COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP
|
#
|
PC Sub | Program Competencies/Outcomes |
* Contribution Level
|
||||
|
1
|
2
|
3
|
4
|
5
|
|||
| 1 | Accesses information in breadth and depth by conducting scientific research in Electrical and Electronics Engineering; evaluates, interprets and applies information. |
-
|
-
|
-
|
-
|
-
|
|
| 2 | Is well-informed about contemporary techniques and methods used in Electrical and Electronics 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. Knows and applies the research methods in studies of the area with a high level of skill. |
-
|
-
|
-
|
-
|
-
|
|
| 4 | Is informed about new and upcoming applications in the field and learns them whenever necessary. |
-
|
-
|
-
|
-
|
-
|
|
| 5 |
Defines and formulates problems related to Electrical and Electronics Engineering, develops methods to solve them and uses progressive methods in solutions. Can independently realize novel studies that bring innovation to the field, or methods, or design, or known methods. |
-
|
-
|
-
|
-
|
-
|
|
| 6 | Develops novel and/or original methods, designs complex systems or processes and develops progressive/alternative solutions in designs. |
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|
| 7 | Designs and implements studies based on theory, experiments and modeling; analyses and resolves the complex problems that arise in this process. Performs critical analysis, synthesis and evaluation of new and complex ideas. |
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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. |
-
|
-
|
-
|
-
|
-
|
|
| 9 | Engages in written and oral communication at least in Level C1 of the European Language Portfolio Global Scale. |
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-
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-
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-
|
-
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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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-
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-
|
-
|
|
| 11 |
Evaluates the results of scientific, technological and engineering research and development activities in terms of the social, environmental, health, safety and legal aspects. Examines social relations and norms related to the field, and develops and makes attempts to change them if necessary. Knows their project management and business applications, and is aware of their limitations in Electrical and Electronics Engineering applications. |
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-
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-
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-
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| 12 |
Highly regards scientific and ethical values in data collection, interpretation, communication and in every professional activity. Adheres to the principles of research and publication ethics. |
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-
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*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest
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