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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS EKO 5077 MODERN HEURISTIC TECHNIQUES ELECTIVE 3 0 0 5

Offered By

Econometrics

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

PROFESSOR IPEK DEVECI KOCAKOÇ

Offered to

Econometrics

Course Objective

The main objective of the course is to give main information about Heuristic Algorithms, to follow new developments about this area and to improve skills to use heuristic algorithms for solving real life problems.

Learning Outcomes of the Course Unit

1   To be able to understand main principles of heuristic techniques. 2   To be able to use heuristic techniques as tools to solve operations research problems 3   To be able to apply genetic algorithms for basic problems 4   To be able to define general information about artificial intelligence optimization.

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 Basic Concepts 3 Traditional Methods-I: Comprehensive search and local search 4 Traditional Methods-II: Greedy Algorithms, Divide and Rule, Dynamic Programing, Branch and Bound, A* Algorithm 5 Simulated Annealing (heat treatment) Algorithm 6 Simulated Annealing Applications 7 Tabu Search 8 Mid-term 9 Applications of Tabu Search 10 Evolutionary Algorithms- Genetic Algorithms 11 Evolutionary Algorithms-II 12 Artificial Neural Networks 13 Ant Colony 14 Ant colony more

Recomended or Required Reading

Tunçhan Tura, Modern Sezgisel Teknikler ve Uygulamalari, Papatya Yayincilik, 2008. Zbigniew Michalewicz, David B. Fogel. How to Solve It: Modern Heuristics. Springer.2004. Derviş Karaboğa, Yapay Zeka Optimizasyon Algoritmaları, Atlas Yayıncılık, 2004.

Planned Learning Activities and Teaching Methods

This course will be presented using class lectures, class discussions, overhead projections, and demonstrations.

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 2 26 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) 131

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