M.SC. in Computer Engineering (Without 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	-
National Occupation Classification	-
Course Coordinator	Dr. Öğr. Üyesi Fehmi Burçin ÖZSOYDAN
Course Lecturer(s)	Dr. Öğr. Üyesi Fehmi Burçin ÖZSOYDAN
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

#	Content	PC Sub	* Contribution Level
#	Content	PC Sub	1	2	3	4	5
1	Understand the basic types evolutionary algorithms, their strengths and weaknesses
2	Use evolutionary algorithms for continuous, binary and combinatorial problems
3	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	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	Weighting	LO 1	LO 2	LO 3
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
#	PC Sub	Program Competencies/Outcomes	1	2	3	4	5
1	Accesses information in breadth and depth by conducting scientific research in Computer Engineering, evaluates, interprets and applies information.		-	-	-	X	-
2	Is well-informed about contemporary techniques and methods used in Computer Engineering and their limitations.		-	-	X	-	-
3	Uses scientific methods to complete and apply information from uncertain, limited or incomplete data, can combine and use information from different disciplines.		-	-	-	X	-
4	Is informed about new and upcoming applications in the field and learns them whenever necessary.		-	-	X	-	-
5	Defines and formulates problems related to Computer Engineering, develops methods to solve them and uses progressive methods in solutions.		-	-	-	-	X
6	Develops novel and/or original methods, designs complex systems or processes and develops progressive/alternative solutions in designs.		-	-	-	-	X
7	Designs and implements studies based on theory, experiments and modelling, analyses and resolves the complex problems that arise in this process.		X	-	-	-	-
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.		-	-	-	-	X
9	Engages in written and oral communication at least in Level B2 of the European Language Portfolio Global Scale.		X	-	-	-	-
10	Communicates the process and the results of his/her studies in national and international venues systematically, clearly and in written or oral form.		-	-	X	-	-
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.		X	-	-	-	-
12	Highly regards scientific and ethical values in data collection, interpretation, communication and in every professional activity.		X	-	-	-	-