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İzmir Ekonomi Üniversitesi

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    GRADUATE SCHOOL

    M.SC. in Computer Engineering (With Thesis)

    IE 530 | Course Introduction and Application Information

    Course Name
    Evolutionary Algorithms
    Code
    Semester
    Theory
    (hour/week)
    Application/Lab
    (hour/week)
    Local Credits
    ECTS
    IE 530
    Fall/Spring
    3
    0
    3
    7.5

    Prerequisites
    None
    Course Language
    English
    Course Type
    Elective
    Course Level
    Second Cycle
    Mode of Delivery -
    Teaching Methods and Techniques of the Course -
    National Occupation Classification -
    Course Coordinator
    Course Lecturer(s)
    Assistant(s) -
    Course Objectives The purpose of this course is to introduce the main concepts and applications in the field of evolutionary computation, with emphasis on evolutionary algorithms and swarm intelligence based problem-solving techniques. It will also provide students with practical experience with evolutionary algorithms for search and optimization..
    Learning Outcomes

    The students who succeeded in this course;

    • Understand the basic types evolutionary algorithms, their strengths and weaknesses
    • Use evolutionary algorithms for continuous, binary and combinatorial problems
    • Possess practical experience in using three algorithms
    Course Description This course teaches basic evolutionary algorithms to the students and helps them gain experience in applying some of them. Among the topics of the course are, theoretical foundations of evolutionary algorithms, genetic algorithms, evolutionary operators, particle swarm optimization, and differential evolution algorithm.

     



    Course Category

    		Core Courses
    Major Area Courses
    Supportive Courses
    Media and Management Skills Courses
    Transferable Skill Courses

     

    WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

    Week Subjects Related Preparation Learning Outcome
    1 Introduction to Evolutionary algorithms
    2 Genetic Algorithms – basics
    3 Genetic Algorithms - operators & selection schemes
    4 Genetic Algorithms - operators & selection schemes
    5 Differential evolution algorithm
    6 Particle swarm optimization
    7 Midterm
    8 New generation bio-inspired algorithms
    9 Evolutionary algorithm applications for continuous spaces
    10 Evolutionary algorithm applications for binary spaces
    11 Evolutionary algorithm applications for combinatorial spaces
    12 Memetic algorithms
    13 Memetic algorithms
    14 Learning schemes & self-adaptation
    15 Review of final and presentations
    16 Review of the Semester and presentations

     

    Course Notes/Textbooks

    Günther Zäpfel, Roland Braune, Michael Bögl (2010). Metaheuristic Search Concepts A Tutorial with Applications to Production and Logistics. Springer.

    Mitsuo Gen, Runwei Cheng, (2000). Genetic Algorithms and Engineering Optimization. Wiley.
    Suggested Readings/Materials

    Zelinka, I. (2015). A survey on evolutionary algorithms dynamics and its complexity–Mutual relations, past, present and future. Swarm and Evolutionary Computation, 25, 2-14.

     

    EVALUATION SYSTEM

    Semester Activities Number Weigthing
    Participation
    Laboratory / Application
    Field Work
    Quizzes / Studio Critiques
    Portfolio
    Homework / Assignments
    Presentation / Jury
    1
    15
    Project
    1
    25
    Seminar / Workshop
    Oral Exams
    Midterm
    1
    20
    Final Exam
    1
    40
    Total

    Weighting of Semester Activities on the Final Grade
    3
    60
    Weighting of End-of-Semester Activities on the Final Grade
    1
    40
    Total

    ECTS / WORKLOAD TABLE

    Semester Activities Number Duration (Hours) Workload
    Theoretical Course Hours
    (Including exam week: 16 x total hours)
    16
    3
    48
    Laboratory / Application Hours
    (Including exam week: '.16.' x total hours)
    16
    0
    Study Hours Out of Class
    14
    5
    70
    Field Work
    0
    Quizzes / Studio Critiques
    0
    Portfolio
    0
    Homework / Assignments
    0
    Presentation / Jury
    1
    18
    18
    Project
    1
    34
    34
    Seminar / Workshop
    0
    Oral Exam
    0
    Midterms
    1
    25
    25
    Final Exam
    1
    30
    30
        Total
    225

     

    COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

    #
    		 PC Sub Program Competencies/Outcomes
    * Contribution Level
    1
    2
    3
    4
    5
    1 Accesses information in breadth and depth by conducting scientific research in Computer Engineering; evaluates, interprets and applies information.
    -
    -
    -
    -
    -
    2 Is well-informed about contemporary techniques and methods used in Computer Engineering and their limitations.
    -
    -
    -
    -
    -
    3 Uses scientific methods to complete and apply information from uncertain, limited or incomplete data; can combine and use information from different disciplines.
    -
    -
    -
    -
    -
    4 Is informed about new and upcoming applications in the field and learns them whenever necessary.
    -
    -
    -
    -
    -
    5 Defines and formulates problems related to Computer Engineering, develops methods to solve them and uses progressive methods in solutions.
    -
    -
    -
    -
    -
    6 Develops novel and/or original methods, designs complex systems or processes and develops progressive/alternative solutions in designs
    -
    -
    -
    -
    -
    7 Designs and implements studies based on theory, experiments and modelling; analyses and resolves the complex problems that arise in this process.
    -
    -
    -
    -
    -
    8 Can work effectively in interdisciplinary teams as well as teams of the same discipline, can lead such teams and can develop approaches for resolving complex situations; can work independently and takes responsibility.
    -
    -
    -
    -
    -
    9 Engages in written and oral communication at least in Level B2 of the European Language Portfolio Global Scale.
    -
    -
    -
    -
    -
    10 Communicates the process and the results of his/her studies in national and international venues systematically, clearly and in written or oral form.
    -
    -
    -
    -
    -
    11 Is knowledgeable about the social, environmental, health, security and law implications of Computer Engineering applications, knows their project management and business applications, and is aware of their limitations in Computer Engineering applications.
    -
    -
    -
    -
    -
    12 Highly regards scientific and ethical values in data collection, interpretation, communication and in every professional activity.
    -
    -
    -
    -
    -

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

     

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