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Information
| Code | EM0024 |
| Name | Modern Heuristics |
| Term | 2022-2023 Academic Year |
| Term | Spring |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | Türkçe |
| Level | Yüksek Lisans Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | |
| Course Instructor |
1 |
Course Goal / Objective
The aim of this course is to inform students about heuristic algorithms with engineering applications.
Course Content
Simulated Annealing, Tabu Search, Genetic Algorithm, Differential Evolution Algorithm, Ant Colony, Artificial Intelligence and Machine Learning Algorithms
Course Precondition
None
Resources
D.E. Goldberg, “Genetic Algorithms in Search, Optimization and Machine Learning”.
Notes
D.E. Goldberg, “Genetic Algorithms in Search, Optimization and Machine Learning”.
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Will be able to have knowledge and understanding of heuristic algorithms; |
| LO02 | Will be able to solve the engineering problems using the heuristic algorithms |
| LO03 | Will be able to present a heuristic algorithm project |
| LO04 | will be able to code heuristics and evaluate their results with the help of a programming language |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Conducts scientific research in industrial engineering, understands, interprets and applies knowledge in his/her field domain both in-depth and in-breadth. | 4 |
| PLO02 | Bilgi - Kuramsal, Olgusal | Acquires detailed knowledge for methods and tools of industrial engineering and their limitations. | 5 |
| PLO03 | Bilgi - Kuramsal, Olgusal | Keeps up with the recent changes and applications in the field of Industrial Engineering and examines and learns these innovations when necessary. | 5 |
| PLO04 | Bilgi - Kuramsal, Olgusal | Identifies, gathers and uses necessary information and data. | 3 |
| PLO05 | Beceriler - Bilişsel, Uygulamalı | Has the ability to develop/propose new and/or original ideas and methods, propose new solutions for designing systems, components or processes. | 3 |
| PLO06 | Beceriler - Bilişsel, Uygulamalı | Designs Industrial Engineering problems, develops new methods to solve the problems and applies them. | 4 |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Designs and performs analytical modeling and experimental research and analyze/solves complex matters emerged in this process. | 5 |
| PLO08 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Works in multi-disciplinary teams, take a leading role and responsibility and develop solutions for complex problems. | |
| PLO09 | Yetkinlikler - Öğrenme Yetkinliği | Completes and applies the knowledge by using limited resources in scientific methods and integrates the knowledge in the field with the knowledge form various disciplines. | 3 |
| PLO10 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Uses a foreign language in verbal and written communication at least B2 level of European Language Portfolio. | |
| PLO11 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Presents his/her research findings systematically and clearly in oral or written forms in national and international platforms. | 2 |
| PLO12 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Understands social and environmental implications of engineering practice. | |
| PLO13 | Yetkinlikler - Öğrenme Yetkinliği | Considers social, scientific and ethical values in data collection, interpretation and announcement processes and professional activities. | 3 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction to Moderns heuristics | Reading lecture notes and references about the subject | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 2 | Simulated Annealing | Reading lecture notes and references about the subject | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 3 | Simulated Annealing with case studies | Reading lecture notes and references about the subject | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 4 | Tabu Search | Reading lecture notes and references about the subject | Öğretim Yöntemleri: Soru-Cevap, Anlatım |
| 5 | Tabu Search with case studies | Reading lecture notes and references about the subject | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 6 | Genetic Algorithm | Reading lecture notes and references about the subject | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 7 | Genetic Algorithm with case studies | Reading lecture notes and references about the subject | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 8 | Midterm | Reading Lecture Notes and reviewing the topics learned | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 9 | Ant Colony | Reading lecture notes and references about the subject | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 10 | Ant Colony with case studies | Reading lecture notes and references about the subject | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 11 | Differential Evolution | Reading lecture notes and references about the subject | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 12 | Differential Evolution with case studies | Reading lecture notes and references about the subject | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 13 | Artificial Intelligence | Reading lecture notes and references about the subject | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 14 | Machine Learning Algorithms | Reading lecture notes and references about the subject | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 15 | Final Exam | Reading Lecture Notes and reviewing the topics learned | Ölçme Yöntemleri: Yazılı Sınav |
| 16 | Presentations for projects | Reading Lecture Notes and reviewing the topics learned | Ölçme Yöntemleri: Proje / Tasarım |
| 17 | Assignment Submission | Reading Lecture Notes and reviewing the topics learned | Ölçme Yöntemleri: Ödev |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 3 | 42 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 5 | 70 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 15 | 15 |
| Final Exam | 1 | 30 | 30 |
| Total Workload (Hour) | 157 | ||
| Total Workload / 25 (h) | 6,28 | ||
| ECTS | 6 ECTS | ||