Natural Computing (CMPE564) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Natural Computing CMPE564 Elective Courses 3 0 0 3 5
Pre-requisite Course(s)
Consent of instructor
Course Language English
Course Type Elective Courses
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.
Course Learning Outcomes The students who succeeded in this course;
  • Gain necessary knowledge about nature-inspired computing mechanisms, including Hill Climbing, Simulated Annealing, Genetic Algorithms, Neural Networks, Swarm Intelligence (e.g. Ant Colonies, Particle Swarm Optimization) and Artificial Immune Systems.
  • Understand and improve the mentioned nature inspired computing techniques
  • Applying the nature-inspired computing techniques to real-life practical problems
  • Develop necessary software codes in the nature-inspired computing context.
Course Content Problem solving by search, hill climbing, simulated annealing, artificial neural networks, genetic algorithms, swarm intelligence (including ant colony optimization and particle swarm optimization), artificial immune systems.

Weekly Subjects and Releated Preparation Studies

Week Subjects Preparation
1 Introduction to Natural Computing Chapter 1 & 2 (Course Book)
2 Introduction to Natural Computing Chapter 1 & 2 (Course Book)
3 Problem Solving by Search; Hill Climbing; Simulated Annealing Chapter 3 (Course Book) and Source #1
4 Evolutionary Computing: Genetic Algorithms. Chapter 3 (Course Book) and Source #1
5 Evolutionary Computing: Genetic Algorithms. Chapter 3 (Course Book) and Source #1
6 Neurocomputing and Artificial Neural Networks Chapter 4 (Course Book) and Source #2
7 Neurocomputing and Artificial Neural Networks Chapter 4 (Course Book) and Source #2
8 Swarm Intelligence: Ant Colony Optimization Chapter 5 (Course Book) and Source #3
9 Swarm Intelligence: Ant Colony Optimization Chapter 5 (Course Book) and Source #3 Chapter 5 (Course Book)
10 Swarm Intelligence: Particle Swarm Optimization Chapter 5 (Course Book)
11 Swarm Intelligence: Particle Swarm Optimization Chapter 5 (Course Book)
12 Artificial Immune Systems Chapter 6 (Course Book)
13 Artificial Immune Systems Chapter 6 (Course Book)
14 Artificial Immune Systems Chapter 6 (Course Book)
15 Review
16 Review

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, ISBN: 0-13-790395-2.
3. J. Hertz, A. Krogh and R.G. Palmer, Introduction to the Theory of Neural Computation, Addison-Wesley Publishing Company, 1991, ISBN: 0-201-50395-6.
4. M. Dorigo and T. Stützle, Ant Colony Optimization, MIT Press, 2004. ISBN: 0-262-04219-3.
5. Artificial Intelligence, Patrick H. Winston, Addison-Wesley, 1992. ISBN: 0-201-533774.

Evaluation System

Requirements Number Percentage of Grade
Attendance/Participation - -
Laboratory - -
Application - -
Field Work - -
Special Course Internship - -
Quizzes/Studio Critics - -
Homework Assignments 2 20
Presentation 1 20
Project - -
Report - -
Seminar - -
Midterms Exams/Midterms Jury 1 20
Final Exam/Final Jury 1 40
Toplam 5 100
Percentage of Semester Work 60
Percentage of Final Work 40
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 An ability to apply knowledge of mathematics, science, and engineering. X
2 An ability to design and conduct experiments, as well as to analyse and interpret data. X
3 An ability to design a system, component, or process to meet desired needs. X
4 An ability to function on multi-disciplinary domains.
5 An ability to identify, formulate, and solve engineering problems. X
6 An understanding of professional and ethical responsibility. X
7 An ability to communicate effectively. X
8 Recognition of the need for, and an ability to engage in life-long learning. X
9 A knowledge of contemporary issues. X
10 An ability to use the techniques, skills, and modern engineering tools necessary for engineering practice. X
11 Skills in project management and recognition of international standards and methodologies X
12 An ability to produce engineering products or prototypes that solve real-life problems. X
13 Skills that contribute to professional knowledge. X
14 An ability to make methodological scientific research. X
15 An ability to produce, report and present an original or known scientific body of knowledge. X
16 An ability to defend an originally produced idea. 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 3 48
Presentation/Seminar Prepration 1 5 5
Project
Report
Homework Assignments 2 5 10
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury 1 10 10
Prepration of Final Exams/Final Jury 1 10 10
Total Workload 131