Course Name Code Semester T+U Hours Credit ECTS
Genetic Algorithms ENM 526 0 3 + 0 3 6
Precondition Courses
Recommended Optional Courses
Course Language Turkish
Course Level yuksek_lisans
Course Type Optional
Course Coordinator Prof.Dr. SEMRA BORAN
Course Lecturers
Course Assistants Res.Assist.Ünal Atakan Kahraman, Res.Asst. Alper Kiraz
Course Category
Course Objective To provide to learn Genetic Algorithm techniques.
Course Content Introduction to Genetic Algorithm, The reasons of use Genetic Algorithm, difference between Genetic Algorithm and the other methods, Fundamental concept of Genetic Algorithm(GA), Genetic Operators ,
What does GA work?, the applications of Engineering problems, Use of Supported Computer GA for the problems, Perform graphics, GA example with Multiple hills.
# Course Learning Outcomes Teaching Methods Assessment Methods
1 To know Genetic Algorithm techniques and need it Lecture, Question-Answer, Discussion, Drilland Practice, Case Study, Problem Solving, Testing, Homework,
2 Able to apply Genetic Algorithm techniques Lecture, Question-Answer, Discussion, Drilland Practice, Case Study, Problem Solving, Testing, Homework,
3 Lecture, Question-Answer, Discussion, Drilland Practice, Case Study, Problem Solving, Testing, Homework,
4 Lecture, Question-Answer, Discussion, Drilland Practice, Case Study, Problem Solving, Testing, Homework,
5 Lecture, Question-Answer, Discussion, Drilland Practice, Case Study, Problem Solving, Testing, Homework,
6 Lecture, Question-Answer, Discussion, Drilland Practice, Case Study, Problem Solving, Testing, Homework,
7 Lecture, Question-Answer, Discussion, Drilland Practice, Case Study, Problem Solving, Testing, Homework,
8 Lecture, Question-Answer, Discussion, Drilland Practice, Case Study, Problem Solving, Testing, Homework,
9 Lecture, Question-Answer, Discussion, Drilland Practice, Case Study, Problem Solving, Testing, Homework,
10 Lecture, Question-Answer, Discussion, Drilland Practice, Case Study, Problem Solving, Testing, Homework,
11 Lecture, Question-Answer, Discussion, Drilland Practice, Case Study, Problem Solving, Testing, Homework,
12 Lecture, Question-Answer, Discussion, Drilland Practice, Case Study, Problem Solving, Testing, Homework,
Week Course Topics Preliminary Preparation
1 Introduction to Genetic Algorithm
2 The reasons of use Genetic Algorithm
3 Difference between Genetic Algorithm and the other methods
4 Fundamental concept of Genetic Algorithm(GA)
5 Genetic Operators
6 The Working GA
7 What does GA work?
8 The applications of Engineering problems
9 Use of Supported Computer GA for the problems
10 The important and application of Mutation Operator
11 What are the Code solution and Perform graphics?
12 Use of Supported Computer GA for the problems
13 Maintenance Scheduling with GA
14 GA example with Multiple hills
Resources
Course Notes
Course Resources
Order Program Outcomes Level of Contribution
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.
2 Ability to complete and apply knowledge by scientific methods using limited or missing data; to integrate information from different disciplines.
3 To be able to construct engineering problems, develop methods to solve them and apply innovative methods in solutions.
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.
6 Identify the information and data needed, reach them and evaluate them at an advanced level.
7 Leadership in multi-disciplinary teams, developing solutions to complex situations and taking responsibility.
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.
9 Interpreting comprehensive information about modern techniques and methods applied in engineering and their limits.
10 Awareness about new and developing practices of the profession; to examine and learn them when necessary.
11 To understand the social and environmental dimensions of engineering applications and to adapt to the social environment.
12 To observe social, scientific and ethical values in the stages of data collection, interpretation and announcement and in all professional activities.
Evaluation System
Semester Studies Contribution Rate
1. Ara Sınav 60
1. Ödev 10
2. Ödev 10
1. Sözlü Sınav 10
1. Performans Görevi (Seminer) 10
Total 100
1. Final 50
1. Yıl İçinin Başarıya 50
Total 100
ECTS - Workload Activity Quantity Time (Hours) Total Workload (Hours)
Course Duration (Including the exam week: 16x Total course hours) 16 3 48
Hours for off-the-classroom study (Pre-study, practice) 16 3 48
Mid-terms 1 10 10
Quiz 1 6 6
Assignment 2 16 32
Performance Task (Laboratory) 1 15 15
Total Workload 159
Total Workload / 25 (Hours) 6.36
dersAKTSKredisi 6