Ders Bilgileri

#### Ders Tanımı

Ders Kodu Yarıyıl T+U Saat Kredi AKTS
HEURISTIC OPTIMIZATION METHODS ENM 546 0 3 + 0 3 6
 Dersin Dili Türkçe Dersin Seviyesi Yüksek Lisans Dersin Türü SECMELI Dersin Koordinatörü Dr.Öğr.Üyesi NEVRA AKBİLEK Dersi Verenler Dr.Öğr.Üyesi NEVRA AKBİLEK Dersin Yardımcıları Dersin Kategorisi Dersin Amacı This course is a survey of the newer, most common adaptive search methods. This undergraduate course with emphasis on self exploration and research. There will be homework assignments, a quiz, an exam and a project. The homework assignments and the project should be done individually. The project can synthesize multiple techniques or be an in depth exploration of one technique using problems and applications are of the student’s choice Dersin İçeriği The areas of focus will be simulated annealing, genetic algorithms, evolutionary strategies, tabu search, ant colony methods and particle swarm optimization. Other methods will be briefly covered. Both combinatorial and continuous optimization problems will be considered, with emphasis on combinatorics. The main techniques will be introduced, discussed critically and variations presented.
 Dersin Öğrenme Çıktıları Öğretim Yöntemleri Ölçme Yöntemleri 1 - 1 - 2 - 3 - B - 2 - 1 - 2 - 15 - C - 3 - 1 - 2 - 3 - 6 - 16 - B - D - 4 - 1 - 2 - 3 - 6 - 16 - A - D - 5 - 1 - 2 - 3 - 6 - 16 - A - D - 6 - 1 - 2 - 3 - 6 - 16 - B - D - 7 - 1 - 2 - 3 - 6 - 16 - A - D - 8 - 1 - 3 - 4 - 12 - A - D - 9 - 1 - 2 - 3 - 4 - 16 - B - D - 10 - 1 - 2 - 3 - 4 - 16 - A - D - 11 - 1 - 2 - 3 - 8 - 16 - A - D - 12 - 1 - 2 - 12 - A - D - 13 - 1 - 2 - 3 - 16 - A - B - D - 14 - 1 - 2 - 3 - 8 - 15 - 1 - 2 - 12 - A -
 Öğretim Yöntemleri: 1:Lecture 2:Question-Answer 3:Discussion 6:Motivations to Show 16:Project Based Learning 4:Drilland Practice 12:Case Study 8:Group Study 15:Problem Solving Ölçme Yöntemleri: B:Oral Exam D:Project / Design A:Testing C:Homework

#### Ders Akışı

Hafta Konular ÖnHazırlık
1 Introduction to Optimiztion Artificial Intelligence-Introduction to Heuristic optimization algorithms
2 Simulated Annealing Metaheuristics for Hard Optimization: Methods and Case Studies Johann Dréo, Alain Pétrowski , Patrick Siarry, Eric Taillard
3 Introduction to Evolutionary Computation Metaheuristics for Hard Optimization: Methods and Case Studies Johann Dréo, Alain Pétrowski , Patrick Siarry, Eric Taillard
4 Evolutionary Strategies Metaheuristics for Hard Optimization: Methods and Case Studies Johann Dréo, Alain Pétrowski , Patrick Siarry, Eric Taillard
5 Optimization and Machine learning Metaheuristics for Hard Optimization: Methods and Case Studies Johann Dréo, Alain Pétrowski , Patrick Siarry, Eric Taillard
6 Quadratic Assignment problem-Short Term Memory Metaheuristics for Hard Optimization: Methods and Case Studies Johann Dréo, Alain Pétrowski , Patrick Siarry, Eric Taillard
7 Long term memory-Tabu Search Metaheuristics for Hard Optimization: Methods and Case Studies Johann Dréo, Alain Pétrowski , Patrick Siarry, Eric Taillard
8 Ant Colony Optimization Metaheuristics for Hard Optimization: Methods and Case Studies Johann Dréo, Alain Pétrowski , Patrick Siarry, Eric Taillard
9 Particle Swarm Optimization Metaheuristics for Hard Optimization: Methods and Case Studies Johann Dréo, Alain Pétrowski , Patrick Siarry, Eric Taillard
10 Some Other Metaheuristics Metaheuristics for Hard Optimization: Methods and Case Studies Johann Dréo, Alain Pétrowski , Patrick Siarry, Eric Taillard
11 Current Heuristic Applications in Literature
12 Implementing one Optimization Method for a real problem as Project
13 Evaluation of Suggested Project
14 Comparison of The Heuristics According to Project Results

#### Kaynaklar

Ders Notu [1] Metaheuristics for Hard Optimization: Methods and Case Studies
Johann Dréo, Alain Pétrowski (Author), Patrick Siarry (Author), Eric Taillard (Author), A. Chatterjee (Translator)
[2] Genetic Algorithms in Search, Optimization and Machine Learning (Goldberg)
[3] Genetic Programming (Koza)
[4] Genetic Algorithms and Simulated Annealing (Davis)
[5] Simulated Annealing and Boltzmann Machines (Aarts and Korst)
[6] Evolution and Optimum Seeking (Schwefel)
Ders Kaynakları

#### Dersin Program Çıktılarına Katkısı

No Program Öğrenme Çıktıları KatkıDüzeyi
1 2 3 4 5
1 The aim of the course is to reach the information in depth and in depth by conducting scientific research in the field of engineering, to evaluate, interpret and apply the information. X
2 Ability to complete and apply knowledge by scientific methods using limited or missing data; to integrate information from different disciplines. X
3 To be able to construct engineering problems, develop methods to solve them and apply innovative methods in solutions. X
4 Ability to develop new and original ideas and methods; develop innovative solutions in system, part or process designs.
5 Ability to design and apply analytical, modeling and experimental research; to analyze and interpret complex situations encountered in this process. X
6 Identify the information and data needed, reach them and evaluate them at an advanced level. X
7 Leadership in multi-disciplinary teams, developing solutions to complex situations and taking responsibility. X
8 To be able to convey the process and results of his / her studies systematically and clearly in written or oral form in national and international environments in or out of that field. X
9 Interpreting comprehensive information about modern techniques and methods applied in engineering and their limits. X
10 To understand the social and environmental dimensions of engineering applications and to adapt to the social environment. X
11 To observe social, scientific and ethical values in the stages of data collection, interpretation and announcement and in all professional activities. X

#### Değerlendirme Sistemi

YARIYIL İÇİ ÇALIŞMALARI SIRA KATKI YÜZDESİ
AraSinav 1 25
Odev 1 5
ProjeTasarim 1 60
PerformansGoreviUygulama 1 10
Toplam 100
Yıliçinin Başarıya Oranı 70
Finalin Başarıya Oranı 30
Toplam 100

#### AKTS - İş Yükü

Etkinlik Sayısı Süresi(Saat) Toplam İş yükü(Saat)
Course Duration (Including the exam week: 16x Total course hours) 16 3 48
Hours for off-the-classroom study (Pre-study, practice) 16 1 16
Mid-terms 1 10 10
Assignment 1 10 10
Project / Design 1 30 30
Performance Task (Application) 1 10 10
Final examination 1 10 10
Toplam İş Yükü 134
Toplam İş Yükü /25(s) 5.36
Dersin AKTS Kredisi 5.36
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