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 | Elective Courses | 3 | 0 | 0 | 3 | 5 |
| Pre-requisite Course(s) |
|---|
| N/A |
| 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 Lecturer(s) |
|
| 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;
|
| 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 | Demonstrates the ability to conduct advanced research activities both individually and as a team member. | |||||
| 2 | Gains the competence to examine, evaluate, and interpret research topics through scientific reasoning. | |||||
| 3 | Develops new methods and applies them to original research areas and topics. | |||||
| 4 | Systematically acquires experimental and/or analytical data, discusses and evaluates them to reach scientific conclusions. | |||||
| 5 | Applies the scientific philosophical approach in the analysis, modeling, and design of engineering systems. | |||||
| 6 | Synthesizes knowledge in their field to create, maintain, complete, and present original studies at an international level. | |||||
| 7 | Contributes to scientific and technological advancements in their engineering field. | |||||
| 8 | Contributes to industrial and scientific progress to improve society through research activities. | |||||
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 | ||