COURSE UNIT TITLE

: GENETIC PROGRAMMING

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
CSE 6008 GENETIC PROGRAMMING 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

ASSOCIATE PROFESSOR ZERRIN IŞIK

Offered to

COMPUTER ENGINEERING
Industrial Ph.D. Program In Advanced Biomedical Technologies
Industrial Ph.D. Program In Advanced Biomedical Technologies
Biomedical Tehnologies (English)
Computer Engineering
Computer Engineering

Course Objective

This course is an introduction to the field of Genetic Programming and its applications to engineering problems, arts and design.

Learning Outcomes of the Course Unit

1   Identify standard Genetic Algorithm
2   Develop solutions to a given engineering problem using Genetic Algorithm
3   Develop genetic programs using LISP programming language for given problems in engineering, arts and design

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction to Evolution, Natural Selection, Genetic Algorithms, LISP
2 The Representation Problem for Genetic Algorithms
3 Overview of Genetic Programming, Examples
4 Genetic Operators
5 Asessing Goodness of Solutions, Fitness Function
6 Quality of Solutions Produced By Genetic Algorithms, Properties of Genetic Algorithms
7 Optimization Problem and Genetic Algorithms
8 Computer Implementation of A Genetic Algorithm
9 Some Applications of Genetic Algorithms
10 Advanced Operators and Techniques in Genetic Search
11 Amount of Processing Required to Solve a Problem, Parallelization
12 Genetics-Based Machine Learning
13 Applications of Genetic Based Machine Learning
14 Review

Recomended or Required Reading

Ana kaynak: Koza, John R. Genetic Programming: On the Programming of Computers by Means of Natural Selection, The MIT Press, 1992.
Yardımcı kaynaklar: Goldberg, David. Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, 1989.
Referanslar: Various articles to be provided to the students
Diğer ders materyalleri: Lecture Notes, problem sets.

Planned Learning Activities and Teaching Methods

Theoretical lectures, developing solutions to given problem sets and independent work done at home, reading assignments.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 PRJ PROJECT
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE MTE * 0.35 + PRJ * 0.15 + FIN * 0.50
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.35 + PRJ * 0.15 + RST * 0.50


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

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)

SSevinc, x1740

Office Hours

TBA

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 3 42
Preparations before/after weekly lectures 14 4 56
Preparation for midterm exam 2 10 20
Preparation for final exam 1 10 10
Preparing assignments 4 6 24
Preparing presentations 2 10 20
Design Project 1 20 20
Final 1 2 2
Midterm 2 2 4
TOTAL WORKLOAD (hours) 198

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.1445
LO.244435
LO.344435

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

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