GRADUATE SCHOOL

M.SC. in Computer Engineering (With Thesis)

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 -
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;
  • Understand the basic types evolutionary algorithms, their strengths and weaknesses
  • Use evolutionary algorithms for continuous, binary and combinatorial problems
  • Possess practical experience in using three algorithms
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.

 



Course Category

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 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

#
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.
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.
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.
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.
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 B2 of the European Language Portfolio Global Scale.
10 Communicates the process and the results of his/her studies in national and international venues systematically, clearly and in written or oral form.
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.
12 Highly regards scientific and ethical values in data collection, interpretation, communication and in every professional activity.

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest

 


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