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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS EKO 6055 HEURISTIC ALGORITHMS ELECTIVE 3 0 0 6

Offered By

Econometrics

Level of Course Unit

Third Cycle Programmes (Doctorate Degree)

Course Coordinator

PROFESSOR DOCTOR MEHMET AKSARAYLI

Offered to

Econometrics

Course Objective

The main objective of the course is to give the basic information about the heuristic algorithms and to be able to follow the new developments in this subject.Hence, it is aimed that students shoul learn important subjects related to heuristic algorithms and they should use them in economic fields.

Learning Outcomes of the Course Unit

1   To be able to understand the basic principle of heuristic techniques . 2   To be able to use the heuristic techniques as a method for operations research. 3   To be able to understand the importance of alternative search algorithms. 4   To be able to integrate classical optimization techniques and heuristic techniques.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Introduction to Heuristic Algorithms 2 The Advantages of evolutionary Algorithms and the Current Developments 3 Modern Search Techniques. 4 Particle Swarm Optimization 5 The Importance Features of Ant Colony Search Algorithms. 6 The Functions and Strategies of Tabu Search Method. 7 The use of Long-Term Memory at Tabu Search. 8 Mid-term 9 The Basics of Simulated Annealing Method 10 The Actual Applications for Simulated Annealing Method 11 Integration of fuzzy logic with evolutionary algorithms 12 Pareto Multi-Objective Optimization 13 Analysis of Applications for Heuristic Algorithms. 14 Analysis of Applications for Heuristic Algorithms.

Recomended or Required Reading

Modern Heuristic Optimization Techniques. Kwang Y. Lee and Mohamed A. El-Sharkawi. IEEE Press. 2008. Zbigniew Michalewicz, David B. Fogel. How to Solve It: Modern Heuristics. Springer.2004.

Planned Learning Activities and Teaching Methods

This course will be presented using methods of expression, discussion and solving problem.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA 1 MTE MIDTERM EXAM 2 STT TERM WORK (SEMESTER) 3 FIN FINAL EXAM 4 FCG FINAL COURSE GRADE MTE * 0.30 + STT * 0.30 + FIN* 0.40 5 RST RESIT 6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.30 + STT * 0.30 + RST* 0.40 *** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

To be announced.

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) Lectures 13 3 39 Preparations before/after weekly lectures 13 3 39 Preparation for midterm exam 1 30 30 Preparation for final exam 1 30 30 Midterm 1 3 3 Final 1 3 3 TOTAL WORKLOAD (hours) 144

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 LO.1 1 LO.2 1 LO.3 1 LO.4 1