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 Ph.D.
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 carry out advanced research activities, both individual and as a member of a team X
2 Ability to evaluate research topics and comment with scientific reasoning X
3 Ability to initiate and create new methodologies, implement them on novel research areas and topics X
4 Ability to produce experimental and/or analytical data in systematic manner, discuss and evaluate data to lead scintific conclusions X
5 Ability to apply scientific philosophy on analysis, modelling and design of engineering systems X
6 Ability to synthesis available knowledge on his/her domain to initiate, to carry, complete and present novel research at international level X
7 Contribute scientific and technological advancements on engineering domain of his/her interest area X
8 Contribute industrial and scientific advancements to improve the society through research activities X

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