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 5024 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

PROFESSOR DOCTOR MURAT ERŞEN BERBERLER

Offered to

Ph.D. in Computer Science (English)
Computer Science
Artificial Intelligence and Intelligent Systems

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 Computer applications
15 Final exam

Recomended or Required Reading

Stuart Russell, Peter Norvig, Artificial Intelligence: A Modern Approach, Pearson, 2010.

D.T. Pham, D. Karaboğa, Intelligent Optimization Techniques, Springer-Verlag, 2000.

Planned Learning Activities and Teaching Methods

The course is taught in a lecture, class presentation and discussion format. Besides the taught lecture, group presentations are to be prepared by the groups assigned and presented in a discussion session. In some weeks of the course, results of the homework given previously are discussed.

Assessment Methods

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


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

To be announced.

Assessment Criteria

To be announced.

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

murat.berberler@deu.edu.tr

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 13 3 39
Project Preparation 1 12 12
Preparation for final exam 1 24 24
Preparations before/after weekly lectures 13 10 130
Project Assignment 1 2 2
Final 1 2 2
TOTAL WORKLOAD (hours) 209

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.15555
LO.25555
LO.35555
LO.45555
LO.55555

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

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