COURSE UNIT TITLE

: ARTIFICIAL INTELLIGENCE OPTIMIZATION ALGORITHMS

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
CSC 5008 ARTIFICIAL INTELLIGENCE OPTIMIZATION ALGORITHMS ELECTIVE 3 0 0 8

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

ASISTANT PROFESSOR MURAT ERŞEN BERBERLER

Offered to

Computer Science
Ph.D. in Computer Science

Course Objective

In this course, the types of artificial intelligence optimization algorithms will be described in detail and experimental results will be held on samples for each type by writing computer programs.Also, assignments will be given that include the artificial intelligence optimization algorithms related to scientific problems that are encountered in practice and should be optimized. The aim of the course is to give knowledge about the literature of the artificial intelligence optimization algorithms and to give skills to bring effective solutions for the problems encountered while using this literature.

Learning Outcomes of the Course Unit

1   Have a knowledge of basic concepts of artificial intelligence optimization algorithms.
2   Be able to solve problems of optimization.
3   Be able to solve computer science problems by using artificial intelligence optimization algorithms concepts.
4   Be able to design efficient algorithms by using artificial intelligence optimization algorithms concepts.
5   Be able to solve problems of different type of disciplines by using concepts of artificial intelligence optimization algorithms.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Fundamental concepts of optimization Classification of optimization problems Classification of optimization methods
2 Heuristic algorithms
3 Simulated annealing algorithm Naturel simulated annealing algorithm
4 Naturel simulated annealing algorithm Programming artificial simulated annealing algorithm and computational experiments
5 Tabu search algorithm Tabu search memory
6 Tabu search strategies Programming tabu search algorithm and computational experiments
7 Genetic algorithms Evolutionary computation
8 Midterm exam
9 Genetic operators Programming genetic algorithm and computational experiments
10 Ant colony algorithm Working principle of ant colony algorithm
11 Programming ant colony algorithm and computational experiments
12 Artificial immune algorithm Working principle of artificial immune algorithm Programming artificial immune algorithm and computational experiments
13 Differential evolution algorithm Working principle of differential evolution algorithm Programming differential evolution algorithm and computational experiments
14 Project evaluation
15 Final exam

Recomended or Required Reading

Derviş Karaboğa, Yapay Zeka Optimizasyon Algoritmaları, Atlas Yayın Dağıtım, 2007.

Tunçhan Cura, Modern Sezgisel Teknikler ve Uygulamaları, Papatya Yayıncılık Eğitim, 2008.

Planned Learning Activities and Teaching Methods

The course is given by the lecturer generally, some periods of the course will continue interactively and the student will hold the project presentation.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 FIN FINAL EXAM
3 FCG FINAL COURSE GRADE MTE * 0.40 + FIN * 0.60
4 RST RESIT
5 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.40 + RST * 0.60


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)

murat.berberler@deu.edu.tr

Office Hours

Will 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 6 78
Preparation for midterm exam 1 12 12
Preparing presentations 1 48 48
Preparation for final exam 1 24 24
Midterm 1 2 2
Final 1 2 2
TOTAL WORKLOAD (hours) 205

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.154
LO.255
LO.34454
LO.4355
LO.544

CONTACT INFORMATION
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Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

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