ECTS - Advanced Natural Computing

Advanced Natural Computing (MDES662) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Advanced Natural Computing MDES662 3 0 0 3 5
Pre-requisite Course(s)
Consent of the instructor
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.
Course Coordinator
Course Lecturer(s)
Course Assistants
Course Objectives The objective of this course is to teach different nature inspired computing techniques; to gain an insight about how to solve real-life practical computing and optimization problems; to gain experience about Simulation and Emulation of Natural Phenomena in Computers, and to become familiar with new natural medium usage in computing.
Course Learning Outcomes The students who succeeded in this course;
  • Gain necessary knowledge about nature-inspired computing mechanisms, including Evolutionary Mechanisms, Ant Colonies, Particle Swarm and Artificial Bee Colonies. Understand the techniques of simulation and emulation of natural phenomena, including cellular automata, L-Systems and artificial life. Applying the nature-inspired computing techniques to real-life practical problems Familiar with the computing with new natural media like DNA.
Course Content Evolutionary computing, ant colony optimization, particle swarm optimization, artificial bee colonies, cellular automata, L-systems, artificial life, DNA computing.

Weekly Subjects and Releated Preparation Studies

Week Subjects Preparation
1 Introduction to Natural Computing Chapter 1 & 2 (Course Book)
2 Evolutionary Computing Chapter 3 (Course Book) and Source #1
3 Evolutionary Computing Chapter 3 (Course Book) and Source #1
4 Swarm Intelligence: Ant Colony Optimization Chapter 5 (Course Book) and Source #2
5 Swarm Intelligence: Ant Colony Optimization Chapter 5 (Course Book) and Source #2
6 Swarm Intelligence: Particle Swarm Optimization Chapter 5 (Course Book) and Source #5
7 Swarm Intelligence: Particle Swarm Optimization Chapter 5 (Course Book) and Source #5
8 Swarm Intelligence: Artificial Bee Colony Algorithm Source #4
9 Simulation and Emulation of Natural Phenomena: Cellular Automata Chapter 7.3 (Course Book)
10 Simulation and Emulation of Natural Phenomena: L-Systems Chapter 7.4 (Course Book)
11 Artificial Life Chapter 8 (Course Book)
12 Artificial Life Chapter 8 (Course Book)
13 Computing on New Medium: DNA Computing Chapter 9 (Course Book)
14 Computing on New Medium: DNA Computing Chapter 9 (Course Book)
15 Overall review -
16 Final exam -


Course Book 1. Leandro Nunes de Castro, Fundamentals of Natural Computing: Basic Concepts, Algorithms and Applications, Chapman & Hall/CRC, 2006, ISBN 1-58488-643-9.
Other Sources 2. S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, Prentice-Hall, 2003
3. M. Dorigo and T. Stützle, Ant Colony Optimization, MIT Press, 2004.
4. Artificial Intelligence, Patrick H. Winston, Addison-Wesley, 1992.

Evaluation System

Requirements Number Percentage of Grade
Attendance/Participation - -
Laboratory - -
Application - -
Field Work - -
Special Course Internship - -
Quizzes/Studio Critics - -
Homework Assignments 1 10
Presentation 1 10
Project 1 30
Report - -
Seminar - -
Midterms Exams/Midterms Jury 1 20
Final Exam/Final Jury 1 30
Toplam 5 100
Percentage of Semester Work 70
Percentage of Final Work 30
Total 100

Course Category

Core Courses X
Major Area Courses
Supportive Courses
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 expand and get in-depth information with scientific researches in the field of mechanical engineering, evaluate information, review and implement.
2 Have comprehensive knowledge about current techniques and methods and their limitations in Mechanical engineering.
3 To complete and apply knowledge by using scientific methods using uncertain, limited or incomplete data; use information from different disciplines.
4 Being aware of the new and developing practices of Mechanical Engineering and being able to examine and learn when needed.
5 Ability to define and formulate problems related to Mechanical Engineering and develop methods for solving and apply innovative methods in solutions.
6 Ability to develop new and/or original ideas and methods; design complex systems or processes and develop innovative/alternative solutions in the designs.
7 Ability to design and apply theoretical, experimental and modeling based researches; analyze and solve complex problems encountered in this process.
8 Work effectively in disciplinary and multi-disciplinary teams, lead leadership in such teams and develop solution approaches in complex situations; work independently and take responsibility.
9 To establish oral and written communication by using a foreign language at least at the level of European Language Portfolio B2 General Level.
10 Ability to convey the process and results of their studies systematically and clearly in written and oral form in national and international environments.
11 To know the social, environmental, health, security, law dimensions, project management and business life applications of engineering applications and to be aware of the constraints of their engineering applications.
12 Ability to observe social, scientific and ethical values in the stages of data collection, interpretation and announcement and in all professional activities.

ECTS/Workload Table

Activities Number Duration (Hours) Total Workload
Course Hours (Including Exam Week: 16 x Total Hours) 16 3 48
Special Course Internship
Field Work
Study Hours Out of Class 16 1 16
Presentation/Seminar Prepration 1 15 15
Project 1 25 25
Homework Assignments 1 15 15
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury 1 8 8
Prepration of Final Exams/Final Jury 1 10 10
Total Workload 137