Heuristic Methods (IE511) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Heuristic Methods IE511 3 0 0 3 5
Pre-requisite Course(s)
N/A
Course Language English
Course Type N/A
Course Level Natural & Applied Sciences Master's Degree
Mode of Delivery Face To Face
Learning and Teaching Strategies Lecture, Question and Answer, Problem Solving.
Course Coordinator
Course Lecturer(s)
  • Asst. Prof. Dr. Babek Erdebilli
Course Assistants
Course Objectives The course aims to teach students fundamental concepts of heuristic models to apply to real-life problems.
Course Learning Outcomes The students who succeeded in this course;
  • Students will have an understanding of the fundamental concepts of heuristic models.
  • Students will develop an insight about the role of heuristic models for various engineering disciplines.
  • Students will be able to solve real life processes and problems using heuristic methods.
  • Students will be familiarized with metaheuristic methods to solve intractable optimization problems.
Course Content The background of heuristic applications for search methods for optimization purposes and the possible application areas, complex optimization problems and possible solving strategies using heuristic algorithms, heuristic search methods, simulated annealing, taboo search general structure, algorithms, application areas, neural networks, general str

Weekly Subjects and Releated Preparation Studies

Week Subjects Preparation
1 The background of heuristic applications for search methods for optimization purposes, the possible application areas.
2 Complex optimization problems and possible solving strategies using heuristic algorithms.
3 Heuristic search methods.
4 Simulated Annealing: general structure, application areas, development of algorithms specific to problems.
5 Taboo Search: general structure, application areas, development of algorithms specific to problems.
6 Neural networks: general structure, application areas, development of algorithms specific to problems.
7 Midterm
8 Discrete and continuous applications.
9 Discrete and continuous applications.
10 Advantages and disadvantages of heuristic search methods for both series and parallel computation in comparison to other optimization algorithms.
11 Practical applications, real life problems
12 Practical applications, real life problems
13 Implementation and term project
14 Implementation and term project
15 Implementation and term project
16 Final Examination Period

Sources

Course Book 1. G. Polya, How to Solve It: A New Aspect of Mathematical Method, Ishi Press, 2009.
Other Sources 2. S. Russell, P. Norvig, Artificial Intelligence: A Modern Approach, Prentice Hall, 2009.
3. Reeves,C. Modern Heuristic Techniques for Combinatorial Problems,Halsted Press, 2003.

Evaluation System

Requirements Number Percentage of Grade
Attendance/Participation - -
Laboratory - -
Application - -
Field Work - -
Special Course Internship - -
Quizzes/Studio Critics - -
Homework Assignments - -
Presentation - -
Project 1 30
Report - -
Seminar - -
Midterms Exams/Midterms Jury 1 30
Final Exam/Final Jury 1 40
Toplam 3 100
Percentage of Semester Work 60
Percentage of Final Work 40
Total 100

Course Category

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

The Relation Between Course Learning Competencies and Program Qualifications

# Program Qualifications / Competencies Level of Contribution
1 2 3 4 5
1 Ability to apply the acquired knowledge in mathematics, science and engineering X
2 Ability to identify, formulate and solve complex engineering problems X
3 Ability to accomplish the integration of systems X
4 Ability to design, develop, implement and improve complex systems, components, or processes X
5 Ability to select/develop and use suitable modern engineering techniques and tools X
6 Ability to design/conduct experiments and collect/analyze/interpret data X
7 Ability to function independently and in teams X
8 Ability to make use of oral and written communication skills effectively X
9 Ability to recognize the need for and engage in life-long learning X
10 Ability to understand and exercise professional and ethical responsibility X
11 Ability to understand the impact of engineering solutions X
12 Ability to have knowledge of contemporary issues X

ECTS/Workload Table

Activities Number Duration (Hours) Total Workload
Course Hours (Including Exam Week: 16 x Total Hours) 16 3 48
Laboratory
Application
Special Course Internship
Field Work
Study Hours Out of Class 16 1 16
Presentation/Seminar Prepration
Project 1 4 4
Report
Homework Assignments 4 4 16
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury 1 16 16
Prepration of Final Exams/Final Jury 1 25 25
Total Workload 125