Instıtute Of Graduate Educatıon
Informatıon Technologıes Master's Program (Wıthout Thesıs)
Course Information
| EVOLUTIONARY COMPUTING AND PROGRAMMING | |||||
|---|---|---|---|---|---|
| Code | Semester | Theoretical | Practice | National Credit | ECTS Credit |
| Hour / Week | |||||
| IT562 | Spring | 3 | 0 | 3 | 5 |
| Prerequisites and co-requisites | None |
|---|---|
| Language of instruction | Turkish |
| Type | Elective |
| Level of Course | Master's |
| Lecturer | Asst. Prof. Dr. Omid SHARIFI |
| Mode of Delivery | Face to Face |
| Suggested Subject | None |
| Professional practise ( internship ) | None |
| Objectives of the Course | Course intends to expose students to concepts of evolutionary computation, biological computing and their related methods and algorithms |
| Contents of the Course | Introduction to Evolution, Natural Selection, Genetic Algorithms, LISP The Representation Problem for Genetic Algorithms Overview of Genetic Programming, Examples Asessing Goodness of Solutions, Fitness Function Quality of Solutions Produced By Genetic Algorithms, Properties of Genetic Algorithms Genetics-Based Machine Learning |
Learning Outcomes of Course
| # | Learning Outcomes |
|---|---|
| 1 | Identify elements of evolutionary computation. |
| 2 | Identify principles of biological computing and their mathematical models. |
| 3 | Develop algorithms and systems based on evolutionary concepts. |
| 4 | Develop algorithms to model and analyze biological processes and systems. |
| 5 | Relate biological systems and behaviour with designed engineering systems. |
Course Syllabus
| # | Subjects | Teaching Methods and Technics |
|---|---|---|
| 1 | Introduction to Evolution, Natural Selection, Genetic Algorithms, LISP | Lecture, discussion, presentation |
| 2 | The Representation Problem for Genetic Algorithms | Lecture, discussion, presentation |
| 3 | Overview of Genetic Programming, Examples | Lecture, discussion, presentation |
| 4 | Genetic Operators | Lecture, discussion, presentation |
| 5 | Asessing Goodness of Solutions, Fitness Function | Lecture, discussion, presentation |
| 6 | Quality of Solutions Produced By Genetic Algorithms, Properties of Genetic Algorithms | Lecture, discussion, presentation |
| 7 | Midterm Exam Review | Exam |
| 8 | Optimization Problem and Genetic Algorithms | Lecture, discussion, presentation |
| 9 | Computer Implementation of A Genetic Algorithm | Lecture, discussion, presentation |
| 10 | Some Applications of Genetic Algorithms | Lecture, discussion, presentation |
| 11 | Advanced Operators and Techniques in Genetic Search | Lecture, discussion, presentation |
| 12 | Amount of Processing Required to Solve a Problem, Parallelization | Lecture, discussion, presentation |
| 13 | Genetics-Based Machine Learning | Lecture, discussion, presentation |
| 14 | Applications of Genetic Based Machine Learning, Review | |
| 15 | ||
| 16 |
Course Syllabus
| # | Material / Resources | Information About Resources | Reference / Recommended Resources |
|---|
Method of Assessment
| # | Weight | Work Type | Work Title |
|---|---|---|---|
| 1 | 40% | Mid-Term Exam | Mid-Term Exam |
| 2 | 60% | Final Exam | Final Exam |
Relationship between Learning Outcomes of Course and Program Outcomes
| # | Learning Outcomes | Program Outcomes | Method of Assessment |
|---|---|---|---|
| 1 | Identify elements of evolutionary computation. | 1͵4͵13 | 1͵2 |
| 2 | Identify principles of biological computing and their mathematical models. | 1͵3͵4͵13 | 1͵2 |
| 3 | Develop algorithms and systems based on evolutionary concepts. | 1͵3͵4͵7͵13 | 1͵2 |
| 4 | Develop algorithms to model and analyze biological processes and systems. | 1͵3͵4͵7͵13 | 1͵2 |
| 5 | Relate biological systems and behaviour with designed engineering systems. | 1͵3͵4͵7͵13 | 1͵2 |
Work Load Details
| # | Type of Work | Quantity | Time (Hour) | Work Load |
|---|---|---|---|---|
| 1 | Course Duration | 14 | 3 | 42 |
| 2 | Course Duration Except Class (Preliminary Study, Enhancement) | 14 | 3 | 42 |
| 3 | Presentation and Seminar Preparation | 0 | 0 | 0 |
| 4 | Web Research, Library and Archival Work | 0 | 0 | 0 |
| 5 | Document/Information Listing | 0 | 0 | 0 |
| 6 | Workshop | 0 | 0 | 0 |
| 7 | Preparation for Midterm Exam | 0 | 0 | 0 |
| 8 | Midterm Exam | 0 | 0 | 0 |
| 9 | Quiz | 0 | 0 | 0 |
| 10 | Homework | 0 | 0 | 0 |
| 11 | Midterm Project | 0 | 0 | 0 |
| 12 | Midterm Exercise | 0 | 0 | 0 |
| 13 | Final Project | 1 | 30 | 30 |
| 14 | Final Exercise | 1 | 6 | 6 |
| 15 | Preparation for Final Exam | 0 | 0 | 0 |
| 16 | Final Exam | 0 | 0 | 0 |
| 120 | ||||