Advanced Natural Computing (MDES662) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Advanced Natural Computing MDES662 Elective Courses 3 0 0 3 5
Pre-requisite Course(s)
Consent of the instructor
Course Language English
Course Type Elective Courses Taken From Other Departments
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 -

Sources

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.
5. http://mf.erciyes.edu.tr/abc/publ.htm
6. http://www.swarmintelligence.org

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
Major Area Courses X
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 Gains the ability to understand and apply knowledge in the fields of mathematics, science and basic sciences at the level of expertise.
2 Gains the ability to access wide and deep knowledge in the field of Engineering by doing scientific research with current techniques and methods, evaluate, interpret and implement the gained knowledge.
3 Being aware of the latest developments his/her field of study, defines problems, formulates and develops new and/or original ideas and methods in solutions.
4 Designs and applies theoretical, experimental, and model-based research, analyzes and interprets the results obtained at the level of expertise.
5 Gains the ability to use the applications, techniques, modern tools and equipment in his/her field of study at the level of expertise.
6 Designs, executes and finalizes an original work process independently.
7 Can work in interdisciplinary and interdisciplinary teams, lead teams, use the information of different disciplines together and develop solution approaches.
8 Pays regard to scientific, social and ethical values in all professional activities and acquires responsibility consciousness at the level of expertise.
9 Contributes to the literature by communicating the processes and results of his/her academic studies in written form or orally in national and international academic environments, communicates effectively with communities and scientific staff working in the field of specialization.
10 Gains the skill of lifelong learning at the level of expertise.
11 Communicates verbally and in written form using a foreign language at least at the European Language Portfolio B2 General Level.
12 Recognizes the social, environmental, health, safety, legal aspects of engineering applications, as well as project management and business life practices, being aware of the limitations they place on engineering applications.

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 1 15 15
Project 1 25 25
Report
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