EM0024 Modern Heuristics

6 ECTS - 3-0 Duration (T+A)- . Semester- 3 National Credit

Information

Code EM0024
Name Modern Heuristics
Term 2024-2025 Academic Year
Semester . Semester
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 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

Update Time: 28.05.2024 02:13