Course Details

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Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
1ECE533INTRODUCTION TO EVOLUTIONARY COMPUTATION3+0+037,514.05.2025

 
Course Details
Language of Instruction English
Level of Course Unit Doctorate's Degree
Department / Program ELECTRICAL AND COMPUTER ENGINEERING
Type of Program Formal Education
Type of Course Unit Elective
Course Delivery Method Face To Face
Objectives of the Course Providing a broader view of common Evolutionary Computing problems.
Discussing recent advancements in the field of Evolutionary Computing.
Showing the implementation approaches of the Evolutionary Computing methods with examples.
Course Content Evolutionary Computation (EC) is a sub-field of Artificial Intelligence and Soft Computing which has been inspired by natural evolution. It can be applied to learning, optimization, design and many more. In this course, students are introduced to the problems of EC, fundamentals of EC concepts, applications of EC, genetic algorithms, evolution strategies, evolution programming, genetic programming, classifier systems, particle swarm optimization, constraint handling, multi-objective cases, memetic algorithms, interactive evolutionary algorithms (EA) and co-evolutionary systems. Active Learning approach is used throughout the course.
Course Methods and Techniques What are the current approaches in Evolutionary Computing?
Problem solving
Projects and reports
Prerequisites and co-requisities None
Course Coordinator Associate Prof.Dr. Rifat Kurban AVESİS rifat.kurban@agu.edu.tr
Name of Lecturers Associate Prof.Dr. Rifat Kurban AVESİS rifat.kurban@agu.edu.tr
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources A.E. Eiben, J.E. Smith, Introduction to Evolutionary Computing, 2015, Springer.
Course Notes A.E. Eiben, J.E. Smith, Introduction to Evolutionary Computing, 2015, Springer.
Documents Will be shared on Canvas.
Assignments Will be shared on Canvas.
Exams Will be shared on Canvas.

Course Category
Mathematics and Basic Sciences %30
Engineering %40
Engineering Design %20
Social Sciences %0
Education %0
Science %0
Health %0
Field %10

Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Ödev 3 % 50
Sunum/Seminer 1 % 50
Total
4
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Araştırma Ödevi 1 63 63
Proje 1 120 120
Senkron Ders 14 3 42
Total Work Load   Number of ECTS Credits 7,5 225

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Explain the fundamental principles of evolutionary computation methods and their relation to natural evolution.
2 Apply evolutionary computation techniques such as genetic algorithms and particle swarm optimization to develop solutions for real-world problems.
3 Analyze and compare various evolutionary computation methods to determine their suitability for specific types of problems.
4 Evaluate the effectiveness of evolutionary algorithms in different application domains by discussing their strengths and weaknesses

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Evrimsel Hesaplamaya Giriş / Introduction to Evolutionary Computation - -
2 Doğal Evrim ve Hesaplama Modelleri / Natural Evolution and Computational Models - -
3 Genetik Algoritmalara Giriş / Introduction to Genetic Algorithms 3 3
4 Genetik Algoritmaların Bileşenleri ve Kodlama Teknikleri / Components of Genetic Algorithms and Encoding Techniques 4 4
5 Evrimsel Stratejiler ve Evrimsel Programlama / Evolution Strategies and Evolutionary Programming 5 5
6 Genetik Programlama / Genetic Programming - -
7 Sınıflandırıcı Sistemler ve Kural Tabanlı Evrimsel Yaklaşımlar / Classifier Systems and Rule-based Evolutionary Approaches - -
8 Ara Sınav Haftası / Midterm Exam Week - -
9 Parçacık Sürü Optimizasyonu (PSO) / Particle Swarm Optimization (PSO) - -
10 Kısıtlarla Başa Çıkma Yöntemleri / Constraint Handling Techniques - -
11 Çok Amaçlı Evrimsel Algoritmalar / Multi-objective Evolutionary Algorithms - -
12 Memetik Algoritmalar ve Yerel Arama ile Entegrasyon / Memetic Algorithms and Integration with Local Search - -
13 Etkileşimli ve Birlikte Evrimsel Sistemler / Interactive and Co-evolutionary Systems - -
14 Evrimsel Hesaplamada Güncel Uygulamalar ve Gelecek Eğilimler / Recent Applications and Future Trends in Evolutionary Computation - -

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11
All 4 5 4 4 4 3 4 1 3 3 3
C1 4 4 3 4 3 2 3 1 2 2 3
C2 3 5 4 4 4 4 4 1 2 3 3
C3 4 5 4 4 5 3 3 1 3 2 2
C4 4 4 4 5 5 4 4 2 4 3 3

  Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant

  
  https://sis.agu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=77813&lang=en
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