EMÜ650 - OPTIMIZATION WITH EVOLUTIONARY ALGORITHMS

Course Name Code Semester Theory
(hours/week)		 Application
(hours/week)		 Credit ECTS
OPTIMIZATION WITH EVOLUTIONARY ALGORITHMS EMÜ650 Any Semester/Year 3 0 3 10
Prequisites
Course languageTurkish
Course typeElective 
Mode of DeliveryFace-to-Face 
Learning and teaching strategiesLecture
Discussion
Question and Answer
Team/Group Work
Problem Solving
Other  
Instructor (s)To be determined by the department 
Course objectiveThe objective of the course is to gain and demonstrate an understanding of Evolutionary Algorithms (EA), to learn main representatives of EAs, to gain knowledge in how EAs can be used for multi-objective optimization, to learn about latest achievements in the field, and to experiment with EAs in different optimization tasks to obtain practical experience. 
Learning outcomes
  1. Explain the underlying principles, theoretical foundations, and application domain of EAs.
  2. Formulate optimization problems for EAs by specifying representations such as binary or real coded, parameters such as population size and termination criteria, and operators such as crossover and mutation.
  3. Describe the main EA representatives in the field such as genetic algorithms, particle swarm optimization and multi-objective evolutionary algorithms, and implement them to solve complex optimization problems.
  4. Define philosophy of evolutionary algorithms in multi-objective optimization, and implement domination and niching principles.
  5. Handle linear and nonlinear constraints with EAs.
  6. By completion of this course; students will be able to ? ? Describe No Free Lunch theorem and evaluate the impact of the settings of EAs.
  7. Effectively understand, present and critique scientific EA papers.
Course Content? Basics of single and multi-objective optimization
? Fundamental concepts of EAs
? Evolutionary search techniques
? Genetic Algorithms
? Particle Swarm Optimization
? Multi-Objective Evolutionary Algorithms
? Constraint Handling Techniques
? No Free Lunch Theorem and Parameter Tuning 
References? Bäck, T. (1996) Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms, Oxford University Press, NY, 1996. ISBN- 978-0-19-509971-3.
? Eiben, A.E., Smith J.E. (2015) Introduction to Evolutionary Computing, 2nd ed. Springer, Berlin, Heidelberg, ISBN- 978-3-662-44873-1.
? Deb, K. (2001) Multi-objective Optimization using Evolutionary Algorithms, Wiley, New York, ISBN: 978-0-471-87339-6. 

Course outline weekly

WeeksTopics
Week 1Optimization, modelling, famous NP problems and fundamentals of evolutionary algorithms
Week 2Components of evolutionary algorithms (representation, mutation and recombination)
Week 3Components of evolutionary algorithms (fitness, selection and population management)
Week 4Main representatives of evolutionary algorithms
Week 5Genetic Algorithms
Week 6Particle Swarm Optimization
Week 7Principles of multi-objective optimization
Week 8Multi-objective evolutionary algorithms
Week 9Multi-objective evolutionary algorithms
Week 10Midterm Exam
Week 11Interactive evolutionary algorithms
Week 12Constraint handling
Week 13No Free Lunch Theorem and parameter tuning
Week 14Project presentations and discussions
Week 15Study for the final exam
Week 16Final Exam

Assesment methods

Course activitiesNumberPercentage
Attendance00
Laboratory00
Application00
Field activities00
Specific practical training00
Assignments00
Presentation110
Project120
Seminar00
Midterms220
Final exam150
Total100
Percentage of semester activities contributing grade succes450
Percentage of final exam contributing grade succes150
Total100

WORKLOAD AND ECTS CALCULATION

Activities Number Duration (hour) Total Work Load
Course Duration (x14) 14 3 42
Laboratory 0 0 0
Application000
Specific practical training000
Field activities000
Study Hours Out of Class (Preliminary work, reinforcement, ect)14684
Presentation / Seminar Preparation12525
Project14646
Homework assignment000
Midterms (Study duration)22244
Final Exam (Study duration) 16060
Total Workload33162301

Matrix Of The Course Learning Outcomes Versus Program Outcomes

D.9. Key Learning OutcomesContrubition level*
12345
1. Reach the necessary knowledge and methods in engineering within the scope of advanced industrial engineering studies through scientific research and evaluate knowledge and methods and implement them.    X 
2. Implement advanced analytical methods and modeling techniques to design processes, products and systems in an innovative and original way and improve them    X
3. Have the competency to plan, manage and monitor processes, products and systems.   X 
4. Evaluate the data obtained from analysis of the processes, products and systems, complete limited or missing data through scientific methods, develop data driven solution approaches.  X  
5. Develop original methods for the efficient integration of the scarce resources such as man, machine, and material, energy, capital and time to the systems and implement these.    X
6. Effectively utilize computer programming languages, computer software, information and communication technology to solve problems in the field of industrial engineering.    X
7. Report and present advanced studies, outcomes/results and the evaluations on the design, analysis, planning, monitoring and improvement of processes, products and systems.   X 
8. Are aware of the professional responsibility, describe the technological, economic and environmental effects of the industrial engineering applications, work as an individual independently and as a team member having an understanding of the scientific ethical values, take responsibility and lead the team. X   
9. Are aware of the up-to-date engineering applications, follow the necessary literature for advanced researches, have the competency to reach knowledge in a foreign language, to quote and implement them.   X 

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest