KIUS
English


COMPUTER ENGINEERING (MS) (ENGLISH) PROGRAMME
COURSE DESCRIPTION
Name of the Course Unit Code Year Semester In-Class Hours (T+P) Credit ECTS Credit
EVOLUTIONARY OPTIMIZATION STRATEGIES CMP511 1 2 3+0 3.0 8.0


General Information
Language of Instruction English
Level of the Course Unit Master's Degree, TYYÇ: Level 7, EQF-LLL: Level 7, QF-EHEA: Second Cycle
Type of the Course Programme Elective
Mode of Delivery of the Course Unit Face-to-face
Work Placement(s) Requirement for the Course Unit No
Coordinator of the Course Unit Dr. RUHSAN ÖNDER
Instructor(s) of the Course Unit
Assistant(s) of the Course Unit

Prerequisites and/or co-requisities of the course unit
CATEGORY OF THE COURSE UNIT
Category of the Course Unit Degree of Contribution (%)
Fundamental Course in the field % 50
Course providing specialised skills to the main field % 50
Course providing supportive skills to the main field -
Course providing humanistic, communication and management skills -
Course providing transferable skills -

Objectives and Contents
Objectives of the Course Unit Discusses the theory, history, mathematics and programming of evolutionary optimization algorithms.
Contents of the Course Unit Prominent algorithms include genetic algorithms, genetic programming, ant colony optimization, particle swarm optimization, differential evolution, biogeography-based optimization and convex optimization techniques.
Contribution of the Course Intending to Provide the Professional Education will learn the theory, history, mathematics and programming of evolutionary optimization algorithms will learn programming with genetic algorithms, genetic programming, ant colony optimization, particle swarm optimization, differential evolution, biogeography-based optimization and convex optimization techniques

No
 Key Learning Outcomes of the Course Unit
On successful completion of this course unit, students/learners will or will be able to:
1 will learn the theory, history, mathematics and programming of evolutionary optimization algorithms
2 will learn programming with genetic algorithms, genetic programming, ant colony optimization, particle swarm optimization, differential evolution, biogeography-based optimization and convex optimization techniques
3 Mathematics and science knowledge and understanding in engineering problems
4 Ability to analyze and interpret data on complex engineering systems in a multidisciplinary context.
5 Ability to identify, formulate and solve unfamiliar complex engineering problems, to apply systems thinking in complex problem solving, to learn independently for life, undertake further work autonomously

Learning Activities & Teaching Methods of the Course Unit
Learning Activities & Teaching Methods of the Course Unit

Weekly Course Contents and Study Materials for Preliminary & Further Study
Week Topics (Subjects) Preparatory & Further Activities
1 Fundamentals of Optimization No file found
2 Classical Evolutionary Algorithms No file found
3 Generic Algorithms, No file found
4 Mathematical Models of Genetic Algorithms, No file found
5 Evolutionary Strategies, No file found
6 Evolutionary Programming, No file found
7 Types of Evolutionary Algorithms No file found
8 Midterm No file found
9 Multimodal Optimization No file found
10 Niching and Sharing Function Optimization No file found
11 Multi Objective Optimization No file found
12 Pareto Optimality No file found
13 Nondominated Sorting Genatic Algorithm for Multiobjective Optimization No file found
14 Use of sharing distances and niche count in Multi objective Genetic Algorithm optimization No file found

SOURCE MATERIALS & RECOMMENDED READING
1- Advanced Optimization by Nature-Inspired Algorithms, Bozorg-Haddad, Omid (Ed.) 2018.

Yardımcı kitap: Richard Szeliski, Convex Optimization, cambridge university press, 2004

MATERIAL SHARING
Course Notes No file found
Presentations No file found
Homework No file found
Exam Questions & Solutions No file found
Useful Links No file found
Video and Visual Materials No file found
Other No file found
Announcements No file found

CONTRIBUTION OF THE COURSE UNIT TO THE PROGRAMME LEARNING OUTCOMES
KNOWLEDGE
Theoretical
No PROGRAMME LEARNING OUTCOMES LEVEL OF CONTRIBUTION*
0 1 2 3 4 5
1 Learning the theory of the constructs such as Advanced Neural Networks, Evolutionary Optimization, Advanced Machine Learning, Advanced Digital Image Processing, Advanced Cryptography, Data Analytics, Advanced Data Mining, Advanced mathematics for engineering studies X
Factual
No PROGRAMME LEARNING OUTCOMES LEVEL OF CONTRIBUTION*
0 1 2 3 4 5
1 GAining knowledge on recent research through literature search and project or report submission assignment on the newly published papers on the theoretical knowledge covered X
SKILLS
Cognitive
No PROGRAMME LEARNING OUTCOMES LEVEL OF CONTRIBUTION*
0 1 2 3 4 5
1 Being able to prepare reports based on research X
2 Being able to present personally implemented work X
Practical
No PROGRAMME LEARNING OUTCOMES LEVEL OF CONTRIBUTION*
0 1 2 3 4 5
1 Implementing programming assignments X
PERSONAL & OCCUPATIONAL COMPETENCES IN TERMS OF EACH OF THE FOLLOWING GROUPS
Autonomy & Responsibility
No PROGRAMME LEARNING OUTCOMES LEVEL OF CONTRIBUTION*
0 1 2 3 4 5
1 Self contained reserach X
2 Being able to present personally conducted research and implementation results X
Learning to Learn
No PROGRAMME LEARNING OUTCOMES LEVEL OF CONTRIBUTION*
0 1 2 3 4 5
1 research X
2 practical programming implementation X
Communication & Social
No PROGRAMME LEARNING OUTCOMES LEVEL OF CONTRIBUTION*
0 1 2 3 4 5
1 Being able to express results of conducted research X
2 Being able to express results of programming implementation X
Occupational and/or Vocational
No PROGRAMME LEARNING OUTCOMES LEVEL OF CONTRIBUTION*
0 1 2 3 4 5
1 Learning methods for data analysis, advanced statistics X
2 Learning programming with new programming languages such as Python X
*Level of Contribution (0-5): Empty-Null (0), 1- Very Low, 2- Low, 3- Medium, 4- High, 5- Very High

No
 Key Learning Outcomes of the Course Unit
On successful completion of this course unit, students/learners will or will be able to: 		PROGRAMME LEARNING OUTCOMES
1 will learn the theory, history, mathematics and programming of evolutionary optimization algorithms 1 (5), 2 (3), 3 (3), 4 (4), 5 (4), 6 (4), 7 (4), 8 (3), 9 (3), 10 (3), 11 (4), 12 (3)
2 will learn programming with genetic algorithms, genetic programming, ant colony optimization, particle swarm optimization, differential evolution, biogeography-based optimization and convex optimization techniques 1 (5), 2 (3), 3 (3), 4 (4), 5 (5), 6 (4), 7 (4), 8 (3), 9 (5), 10 (3), 11 (5), 12 (3), 13 (5)
3 Mathematics and science knowledge and understanding in engineering problems 1 (5), 2 (3), 3 (3), 4 (4), 5 (4), 6 (4), 7 (4), 8 (3), 9 (3), 10 (3), 11 (4), 12 (3)
4 Ability to analyze and interpret data on complex engineering systems in a multidisciplinary context. 1 (5), 2 (5), 3 (5), 4 (5), 5 (4), 6 (5), 7 (5), 8 (5), 9 (3), 10 (5), 11 (4), 12 (3)
5 Ability to identify, formulate and solve unfamiliar complex engineering problems, to apply systems thinking in complex problem solving, to learn independently for life, undertake further work autonomously 1 (5), 2 (3), 3 (3), 4 (4), 5 (4), 6 (4), 7 (4), 8 (3), 9 (3), 10 (3), 11 (4), 12 (3)

Assessment
Assessment & Grading of In-Term Activities Number of
Activities		 Degree of Contribution (%)
Mid-Term Exam 0 -
Computer Based Presentation 0 -
Short Exam 0 -
Presentation of Report 0 -
Homework Assessment 0 -
Oral Exam 0 -
Presentation of Thesis 0 -
Presentation of Document 0 -
Expert Assessment 0 -
Board Exam 0 -
Practice Exam 0 -
Year-End Final Exam 0 -
Internship Exam 0 -
TOTAL 0 %100
Contribution of In-Term Assessments to Overall Grade 0 %50
Contribution of Final Exam to Overall Grade 1 %50
TOTAL 1 %100


WORKLOAD & ECTS CREDITS OF THE COURSE UNIT
Workload for Learning & Teaching Activities
Type of the Learning Activites Learning Activities
(# of week)		 Duration
(hours, h)		 Workload (h)
Lecture & In-Class Activities 14 3 42
Preliminary & Further Study 14 2 28
Land Surveying 0 0 0
Group Work 0 0 0
Laboratory 0 0 0
Reading 0 0 0
Assignment (Homework) 3 5 15
Project Work 1 20 20
Seminar 0 0 0
Internship 0 0 0
Technical Visit 0 0 0
Web Based Learning 0 0 0
Implementation/Application/Practice 3 20 60
Practice at a workplace 0 0 0
Occupational Activity 0 0 0
Social Activity 0 0 0
Thesis Work 0 0 0
Field Study 0 0 0
Report Writing 3 5 15
Total Workload for Learning & Teaching Activities - - 180
Workload for Assessment Activities
Type of the Assessment Activites # of Assessment Activities
 Duration
(hours, h)		 Workload (h)
Final Exam 1 3 3
Preparation for the Final Exam 1 20 20
Mid-Term Exam 0 0 0
Preparation for the Mid-Term Exam 0 0 0
Short Exam 0 0 0
Preparation for the Short Exam 0 0 0
Total Workload for Assessment Activities - - 23
Total Workload of the Course Unit - - 203
Workload (h) / 25.5 8.0
ECTS Credits allocated for the Course Unit 8.0

EBS : Kıbrıs İlim Üniversitesi Eğitim Öğretim Bilgi Sistemi Kıbrıs İlim Üniversitesi AKTS Bilgi Paketi AKTS Bilgi Paketi ECTS Information Package Avrupa Kredi Transfer Sistemi (AKTS/ECTS), Avrupa Yükseköğretim Alanı (Bologna Süreci) hedeflerini destekleyen iş yükü ve öğrenme çıktılarına dayalı öğrenci/öğrenme merkezli öğretme ve öğrenme yaklaşımı çerçevesinde yükseköğretimde uluslarası saydamlığı arttırmak ve öğrenci hareketliliği ile öğrencilerin yurtdışında gördükleri öğrenimleri kendi ülkelerinde tanınmasını kolaylaştırmak amacıyla Avrupa Komisyonu tarafından 1989 yılında Erasmus Programı (günümüzde Yaşam Boyu Öğrenme Programı) kapsamında geliştirilmiş ve Avrupa ülkeleri tarafından yaygın olarak kabul görmüş bir kredi sistemidir. AKTS, aynı zamanda, yükseköğretim kurumlarına, öğretim programları ve ders içeriklerinin iş yüküne bağlı olarak kolay anlaşılabilir bir yapıda tasarlanması, uygulanması, gözden geçirilmesi, iyileştirilmesi ve bu sayede yükseköğretim programlarının kalitesinin geliştirilmesine ve kalite güvencesine önemli katkı sağlayan bir sistematik yaklaşım sunmaktadır. ETIS : İstanbul Aydın University Education & Training System Cyprus Science University ECTS Information Package ECTS Information Package European Credit Transfer and Accumulation System (ECTS) which was introduced by the European Council in 1989, within the framework of Erasmus, now part of the Life Long Learning Programme, is a student-centered credit system based on the student workload required to achieve the objectives of a programme specified in terms of learning outcomes and competences to be acquired. The implementation of ECTS has, since its introduction, has been found wide acceptance in the higher education systems across the European Countries and become a credit system and an indispensable tool supporting major aims of the Bologna Process and, thus, of European Higher Education Area as it makes teaching and learning in higher education more transparent across Europe and facilitates the recognition of all studies. The system allows for the transfer of learning experiences between different institutions, greater student mobility and more flexible routes to gain degrees. It also offers a systematic approach to curriculum design as well as quality assessment and improvement and, thus, quality assurance.