COURSE UNIT TITLE

: HEURISTIC SEARCH METHODS

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
STA 6053 HEURISTIC SEARCH METHODS ELECTIVE 3 0 0 5

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

PROFESSOR DOCTOR UMAY ZEYNEP UZUNOĞLU KOÇER

Offered to

Statistics (English)
STATISTICS (ENGLISH)
Statistics (English)

Course Objective

This course aims to cover various heuristic and meta heuristic search approaches for solving difficult combinatorial optimization problems. It gives basic definitions and introduces the students some basic applications of the heuristic search methods.

Learning Outcomes of the Course Unit

1   Defining basic concepts of heuristic search methods
2   Classifying the heuristic search methods according to different basic properties
3   Explaining heuristic search methods with examples
4   Suggesting solutions for the heuristic search methods
5   Giving offers in order to solving difficult combinatorial optimization problems using the heuristic search methods

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction
2 Categorization of Heuristics
3 Construction heuristics
4 Classical improving search
5 Simulated annealing
6 Tabu search
7 MIDTERM
8 Genetic algorithms
9 Ant colonies, Presentation
10 Constraint handling, Lagrangean Relaxation, Presentation
11 Applications of heuristic search in multiobjective combinatorial optimization
12 Applications of heuristic search in multiobjective combinatorial optimization, Homework
13 Evaluation of heuristic performance, Homework
14 Computational complexity of heuristics

Recomended or Required Reading

Textbook(s):
D.Dasgupta and Z. Michalewicz, Evolutionary Algorithms in Engineering Applications, Springer-Verlag, NY (1997).

Supplementary Book(s):
E. Aarts and J.K. Lenstra (eds.), Local Search in Combinatorial Optimization, Chichester, UK, (1997).
T. Bäck, Evolutionary Algorithms in Theory and Practice, Oxford University Press, UK (1996).

Planned Learning Activities and Teaching Methods

Lecture and problem solving

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 ASG ASSIGNMENT
3 PRS PRESENTATION
4 FIN FINAL EXAM
5 FCG FINAL COURSE GRADE MTE* 0.30 + ASG * 0.20 + PRS * 0.10 + FIN * 0.40
6 RST RESIT
7 FCGR FINAL COURSE GRADE (RESIT) MTE* 0.30 + ASG * 0.20 + PRS * 0.10 + RST * 0.40


Further Notes About Assessment Methods

None

Assessment Criteria

Evaluation of midterm, presentation, homework, and final exam.

Language of Instruction

English

Course Policies and Rules

Student responsibilities:
Attendance to at least 70% for the lectures is an essential requirement of this course and is the responsibility of the student. It is necessary that attendance to the lecture and homework delivery must be on time. Any unethical behavior that occurs either in presentations or in exams will be dealt with as outlined in school policy.

Contact Details for the Lecturer(s)

DEU Fen Fakültesi Istatistik Bölümü
e-mail: umay.uzunoglu@deu.edu.tr
Tel: 0232 301 85 60

Office Hours

It will be announced when the course schedule of the faculty is determined

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 3 42
Preparing presentations 2 8 16
Preparations before/after weekly lectures 14 3 42
Preparation for final exam 1 10 10
Preparation for midterm exam 1 5 5
Preparing assignments 2 8 16
Final 1 2 2
Midterm 1 2 2
TOTAL WORKLOAD (hours) 135

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.12
LO.22
LO.32444
LO.4235444
LO.5244544