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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS CSE 6013 ADVANCED EVOLUTIONARY COMPUTATION 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 Computer Science Computer Engineering Ph.D. in Computer Science Computer Engineering

Course Objective

Course intends to expose students to concepts of evolutionary computation, biological computing and their related methods and algorithms

Learning Outcomes of the Course Unit

1   Identify elements of evolutionary computation. 2   Identify principles of biological computing and their mathematical models. 3   Develop algorithms and systems based on evolutionary concepts. 4   Develop algorithms to model and analyze biological processes and systems. 5   Relate biological systems and behaviour with designed engineering systems.

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 Midterm Exam Review 8 Optimization Problem and Genetic Algorithms 9 Computer Implementation of A Genetic Algorithm 10 Some Applications of Genetic Algorithms 11 Advanced Operators and Techniques in Genetic Search 12 Amount of Processing Required to Solve a Problem, Parallelization 13 Genetics-Based Machine Learning 14 Applications of Genetic Based Machine Learning, Review

Recomended or Required Reading

Textbook(s): Supplementary Book(s): References: 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 MD Midterm 2 AS1 Assignment1 3 AS2 Assignment2 4 FN Final 5 BNS BNS MD * 0.30 + AS1 +AS2/2 * 0.20 + FN * 0.50 6 BUT Bütünleme 7 BBN BBN MD * 0.30 + AS1 +AS2/2 * 0.20 + BUT * 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)

Suleyman Sevinc suleyman.sevinc@deu.edu.tr 0232 301 7403 / 7401

Office Hours

TBA in the first lecture

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) Lectures 39 1 39 Preparations before/after weekly lectures 13 5 65 Preparation for midterm exam 2 10 20 Preparation for final exam 1 15 15 Preparation for quiz etc. 3 3 9 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) 200

Contribution of Learning Outcomes to Programme Outcomes

PO/LO PO.1 PO.2 PO.3 PO.4 PO.5 PO.6 PO.7 PO.8 PO.9 PO.10 LO.1 5 3 5 LO.2 5 3 5 LO.3 5 3 5 LO.4 5 4 5 LO.5 5 5