• HOME
• ABOUT US
  • Name Address
  • General Description
  • Governance
  • Academic Calendar
  • Degree Programmes
  • List of Programmes in English
  • General Admission and Registration Procedures
  • Credit Mobility and Recognition of Prior Learning
  • ECTS Credit Allocation
  • Academic Guidance
  • Contacts
• Degree Programmes
  • Third Cycle Programmes (Doctorate Degree)
  • Second Cycle Programmes (Master's Degree)
  • First Cycle Programmes (Bachelor's Degree)
  • Short Cycle Programmes (Associate's Degree)
• GENERAL INFORMATION FOR STUDENTS
  • Cost of Living
  • Accommodation
  • Meals
  • Medical Facilities
  • Facilities for Disabled Students
  • Insurance
  • Financial Supports
  • Student Affairs Office
  • Practical Information for International Students
  • Language Courses
  • Work Placement Possibilities
  • Sports, Social and Cultural Facilities
  • Student Associations and Clubs
  • About İzmir - Turkey
• INTERNATIONAL RELATIONS
  • International Relations Office
  • Registration Procedures
  • Agreements
  • Coordinators
  • Documents
  • List of Programmes in English
  • ECTS / DS Labels

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS IND 3982 INTRODUCTION TO COMPUTATIONAL INTELLIGENCE ELECTIVE 3 0 0 5

Offered By

Industrial Engineering

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

PROFESSOR DOCTOR ŞENER AKPINAR

Offered to

Industrial Engineering

Course Objective

Learning Outcomes of the Course Unit

1   Having basic knowledge about metaheuristic algorithms. 2   To have knowledge of what arrangements metaheuristic algorithms require in order to be used in solving optimization problems. 3   Gaining the ability to adapt Genetic Algorithm, Ant Colony Optimization and Simulated Annealing Algorithms to solve optimization problems. 4   Gaining the ability to code Genetic Algorithm, Ant Colony Optimization and Simulated Annealing Algorithms in the computer environment to solve optimization problems. 5   Gaining the ability to adapt and code metaheuristic algorithms for solving optimization problems.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Coding in computer environment 2 Coding in computer environment 3 Coding in computer environment 4 Metaheuristic Algorithms 5 Metaheuristic Algorithms 6 Genetic Algorithm 7 Genetic Algorithm 8 Genetic Algorithm 9 Ant Colony Optimization 10 Ant Colony Optimization 11 Ant Colony Optimization 12 Simulated Annealing Algorithm 13 Simulated Annealing Algorithm 14 Simulated Annealing Algorithm

Recomended or Required Reading

1. Glover, F. W., & Kochenberger, G. A. (Eds.). (2006). Handbook of metaheuristics (Vol. 57). Springer Science & Business Media. 2. Talbi, E. G. (2009). Metaheuristics: from design to implementation. John Wiley & Sons. 3. Siarry, P. (Ed.). (2016). Metaheuristics. Cham, Switzerland: Springer International Publishing. 4. Shah, P., Sekhar, R., Kulkarni, A. J., & Siarry, P. (Eds.). (2021). Metaheuristic algorithms in industry 4.0. CRC Press.

Planned Learning Activities and Teaching Methods

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA 1 ASG ASSIGNMENT 2 FIN FINAL EXAM 3 FCG FINAL COURSE GRADE ASG * 0.50 + FIN * 0.50 4 RST RESIT 5 FCGR FINAL COURSE GRADE (RESIT) ASG * 0.50 + RST * 0.50

Further Notes About Assessment Methods

None

Assessment Criteria

Project (50%) + Final Exam (50%)

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

sener.akpinar@deu.edu.tr

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) Lectures 14 3 42 Preparations before/after weekly lectures 14 2 28 Preparation for midterm exam 1 15 15 Preparation for final exam 1 25 25 Preparing presentations 1 15 15 Final 1 2 2 Midterm 1 2 2 TOTAL WORKLOAD (hours) 129

Contribution of Learning Outcomes to Programme Outcomes

PO/LO PO.1 PO.2 PO.3 PO.4 PO.5 PO.6 PO.7 PO.8 PO.9 PO.10 PO.11 LO.1 2 LO.2 2 LO.3 3 LO.4 3 LO.5 3 3