COURSE UNIT TITLE

: SOFT COMPUTING TECHNIQUES

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
ERA 0001 SOFT COMPUTING TECHNIQUES ELECTIVE 2 2 0 5

Offered By

Faculty Of Science

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASSISTANT PROFESSOR AYŞE ÖVGÜ KINAY

Offered to

Mathematics (English)
Computer Science
Biology
Physics
Chemistry
Statistics

Course Objective

This course aims to learn: introduction to soft computing, genetic algorithm, simulated annealing, and swarm algorithms.

Learning Outcomes of the Course Unit

1   Have a basic knowledge of soft computing techniques.
2   Have a basic knowledge of genetic algorithms.
3   Have a basic knowledge of evolutionary algorithms.
4   Have a basic knowledge of swarm intelligence algorithms.
5   Have ability to construct models with soft computing.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction to soft computing techniques
2 Simulated annealing algorithm
3 Genetic algorithm
4 Genetic algorithm (continue)
5 Evolutionary computation
6 Genetic programming
7 Project presentation
8 Review of topics
9 Swarm Intelligence
10 Partical Swarm Intelligence
11 Ant colony optimization
12 Ant colony optimization applications
13 Artificial bee colony optimization
14 Artificial bee colony optimization applications

Recomended or Required Reading

Textbook(s):
Neural Network, Fuzzy Logic, and Genetic Algorithms - Synthesis and Applications", by S. Rajasekaran and G.A. Vijayalaksmi Pai, (2005), Prentice Hall,
Soft Computing and Intelligent Systems - Theory and Application , by Naresh K. Sinha and Madan M. Gupta (2000), Academic Press,
Supplementary Book(s):
Soft Computing and Intelligent Systems Design - Theory, Tools and Applications", by Fakhreddine karray and Clarence de Silva (2004), Addison Wesley
Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence by J. S. R. Jang, C. T. Sun, and E. Mizutani, (1996), Prentice Hall.

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 VZ Vize
2 FN Final
3 BNS BNS VZ * 0.40 + FN * 0.60
4 BUT Bütünleme Notu
5 BBN Bütünleme Sonu Başarı Notu VZ * 0.40 + BUT * 0.60


Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

ovgu.tekin@deu.edu.tr

Office Hours

Will be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 2 28
Tutorials 14 2 28
Preparations before/after weekly lectures 14 3 42
Preparation for midterm exam 1 10 10
Preparation for final exam 1 15 15
Preparing assignments 1 10 10
Final 1 2 2
Midterm 1 2 2
TOTAL WORKLOAD (hours) 137

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13
LO.1555
LO.255
LO.34
LO.4555
LO.5555

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

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