11111

COURSE INTRODUCTION AND APPLICATION INFORMATION


ce.cs.ieu.edu.tr

Course Name
Code
Semester
Theory
(hour/week)
Application/Lab
(hour/week)
Local Credits
ECTS
Fall/Spring
Prerequisites
 ISE 203To succeed (To get a grade of at least DD)
Course Language
Course Type
Elective
Course Level
-
Mode of Delivery -
Teaching Methods and Techniques of the Course
Course Coordinator -
Course Lecturer(s) -
Assistant(s) -
Course Objectives
Learning Outcomes The students who succeeded in this course;
  • Will be able to list the basic heuristic methods for optimization
  • Will be able to compare and contrast these methods with classical optimization methods
  • Will be able to list basic meta-heuristic methods for optimization
  • Will be able to adapt these heuristic methods especially to Industrial Systems Engineering problems
  • Will be able to improve these heuristic methods adapted to Industrial Systems Engineering problems
  • Will be able to implement Improve these heuristic methods adapted to Industrial Systems Engineering problems
Course Description

 



Course Category

Core Courses
Major Area Courses
Supportive Courses
X
Media and Managment Skills Courses
Transferable Skill Courses

 

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week Subjects Required Materials
1 Reminder for Optimization Reading: Textbook (Michalewicz) introduction Ch 1
2 Introduction to complexity and heuristics Lecture notes/slides provided
3 Simulated Annealing Lecture notes/ slides provided, Reading: Related textbook (Michalewicz) chapter 5 and Handbook of Metaheuristics Ch 8
4 Particle Swarm Optimization Lecture notes/slides provided
5 Genetic Algorithms and Evolutionary Strategies 1 Lecture notes/slides provided, Reading: Related textbook (Michalewicz) chapter 5 and Handbook of Metaheuristics Ch 3
6 Genetic Algorithms and Evolutionary Strategies 1 Lecture notes/slides provided, Reading: Related textbook (Michalewicz) chapter 5 and Handbook of Metaheuristics Ch 3
7 MIDTERM
8 Ant Colony Optimization Lecture notes/slides provided, Reading: Handbook of Metaheuristics Ch 9
9 Tabu Search Lecture notes/slides provided, Reading: Related textbook (Michalewicz) chapter 5 and Handbook of Metaheuristics Ch 2
10 BAYRAM
11 GRASP Lecture notes/slides provided Handbook of Metaheuristics Ch 8
12 Scatter Search Lecture notes/slides provided Handbook of Metaheuristics Ch 1
13 Local Search 1 Lecture notes/slides provided Handbook of Metaheuristics Ch 11
14 Local Search 2 Neighbourhoods, VNS Lecture notes/slides provided Reading: Handbook of MetaheuristicsCh 6
15 Review of Final Lecture notes/slides provided
16 Review of the Semester  
Course Notes/Textbooks Textbook:Zbigniew Michalewicz, David B. Fogel “How to Solve It: Modern Heuristics
Suggested Readings/Materials "Handbook of Metaheuristics" edt by: Glover F.,, Kochenberger G.A., Kluwer, 2003 and Lecture PowerPoint slides

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
5
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
5
20
Presentation / Jury
Project
1
35
Seminar / Workshop
Oral Exam
Midterm
1
20
Final Exam
1
20
Total

Weighting of Semester Activities on the Final Grade
80
Weighting of End-of-Semester Activities on the Final Grade
20
Total

ECTS / WORKLOAD TABLE

Semester Activities Number Duration (Hours) Workload
Course Hours
(Including exam week: 16 x total hours)
16
3
48
Laboratory / Application Hours
(Including exam week: 16 x total hours)
16
Study Hours Out of Class
15
2
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
5
2
Presentation / Jury
Project
1
14
Seminar / Workshop
Oral Exam
Midterms
1
8
Final Exams
1
10
    Total
120

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
Program Competencies/Outcomes
* Contribution Level
1
2
3
4
5
1

Adequate knowledge in Mathematics, Science and Computer Engineering; ability to use theoretical and applied information in these areas to model and solve Computer Engineering problems

X
2

Ability to identify, define, formulate, and solve complex Computer Engineering problems; ability to select and apply proper analysis and modeling methods for this purpose

X
3

Ability to design a complex computer based system, process, device or product under realistic constraints and conditions, in such a way as to meet the desired result; ability to apply modern design methods for this purpose

X
4

Ability to devise, select, and use modern techniques and tools needed for Computer Engineering practice

X
5

Ability to design and conduct experiments, gather data, analyze and interpret results for investigating Computer Engineering problems

X
6

Ability to work efficiently in Computer Engineering disciplinary and multi-disciplinary teams; ability to work individually

7

Ability to communicate effectively in Turkish, both orally and in writing; knowledge of a minimum of two foreign languages

8

Recognition of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself

9

Awareness of professional and ethical responsibility

10

Information about business life practices such as project management, risk management, and change management; awareness of entrepreneurship, innovation, and sustainable development

11

Knowledge about contemporary issues and the global and societal effects of engineering practices on health, environment, and safety; awareness of the legal consequences of Computer Engineering solutions

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

 

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