COURSE UNIT TITLE

: EVOLUTIONARY COMPUTATION

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
CSE 5002 EVOLUTIONARY COMPUTATION ELECTIVE 3 0 0 9

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

ASSOCIATE PROFESSOR ZERRIN IŞIK

Offered to

Industrial Ph.D. Program In Advanced Biomedical Technologies
Industrial Ph.D. Program In Advanced Biomedical Technologies
Computer Engineering (Non-Thesis-Evening) (English)
Computer Engineering Non-Thesis (English)
Biomedical Tehnologies (English)
Computer Engineering (English)
Computer Engineering (English)
COMPUTER ENGINEERING (ENGLISH)

Course Objective

This course provides an introduction to how biological systems may be used for processing and storing information. Evolution and its products will be examined as bases for computing. Our primary aim is to identify candidate biological processes from which engineering lessons and perhaps even some products may be derived.

Learning Outcomes of the Course Unit

1   Identify evolution as a model for computing and design.
2   Define how information flows in biological systems.
3   Identify biological units and processes of computing.
4   Explain mathematical concepts and computing concepts which can be applied to model biological computing.
5   Develop algorithms and systems based on biological computing concepts.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Biological concepts; Evolution, Cell, DNA, RNA, Protein, Neurons
2 Introduction to bioelectricity
3 Bioelectric potentials and currents
4 Cell networks and biological information flow
5 Neural Networks and biological computational models
6 Cochlear implant, processing of audio & visual information
7 DNA and genetics
8 DNA-Based Computing
9 Theoretical aspects of computing
10 Models of Molecular Computation
11 Machine Learning and biological learning
12 Human Brain
13 Brain : Functional areas, Biological Computers Bilgisayarlar (ex-vivo)
14 Project Presentations
15 Porject Presentations

Recomended or Required Reading

Supplementary Book(s):
Lamm Ehud, Unger Ron., Biological Computation, Chapman and Hall, 2011.
Plonsey, Robert., Barr Roger C. Bioelectricity A Quantitative Approach, Kluwer Publications, 2000.
Pevsner, Jonathan. Bioinformatics and Functional Genetics, Wiley-Liss, 2003.
Amos, Martyn. Theoretical and Experimental DNA Computation, Springer-Verlag, 2005.
Koza, John R. Genetic Programming: On the Programming of Computers by Means of Natural Selection, The MIT Press, 1992.
Goldberg, David. Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, 1989.

Materials: Lecture Notes,problem sets.

Planned Learning Activities and Teaching Methods

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 PRJ PROJECT
2 FCG FINAL COURSE GRADE PRJ * 1


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)

To be announced.

Office Hours

TBA

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 15 3 45
Preparations before/after weekly lectures 15 6 90
Project Preparation 1 30 30
Preparing assignments 4 4 16
Reading 3 7 21
Final 1 2 2
Midterm 2 2 4
Quiz etc. 3 3 9
TOTAL WORKLOAD (hours) 217

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.143
LO.2445
LO.344435
LO.44445
LO.545335

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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