CSCI 4560/6560 Evolutionary Computation and Its Applications

Fall 2004: Tuesdays and Thursdays 3:30pm - 4:45pm, Mondays 3:35pm - 4:25pm, Boyd GSRC 306

Instructor: Prof. Khaled Rasheed
Telephone: 542-3444
Office Hours: Monday: 4:30-6:00pm & Friday: 3-6pm
Office Location: Room 219B, Boyd GSRC
Email: khaled@cs.uga.edu

Teaching Assistant: Liang Shi
Office Hours: Wednesday: 2:30pm-4:30pm
Office Location: Room 307, Boyd GSRC
Email: shi@cs.uga.edu


Objectives:

To provide a broad introduction to the field of Genetic Algorithms (GA) and other fields of Evolutionary Computation and global optimization. To teach students how to apply these methods to solve problems in complex domains. The course is appropriate both for students preparing for research in Evolutionary Computation, as well as Science and Engineering students who want to apply Evolutionary Computation techniques to solve problems in their fields of study.

Recommended Background:

CSCI 2720 Data Structures (or permission of the instructor). Familiarity with basic computer algorithms and data structures and at least one high level programming language.

Topics to be Covered:

Genetic Algorithm core topics including representation, operators and architectures. Other fields of evolutionary computation. Evolutionary Computation applications in science and Engineering. Other global optimization techniques such as simulated annealing.

Expected Work:

Reading; assignments (including programming); midterm; final; and term project and paper. (Unless otherwise announced by the instructor: all assignments and all exams must be done entirely on your own.)

Academic Honesty and Integrity:

All students are responsible for maintaining the highest standards of honesty and integrity in every phase of their academic careers. The penalties for academic dishonesty are severe and ignorance is not an acceptable defense.

Grading Policy:

·  Assignments: 30% (Programs, questions, paper presentations)

·  Midterm Examination: 20%

·  Final Examination: 25%

·  Term Project: 25% (includes term paper and presentation)
Students may work on their term projects individually or in groups of up to THREE students each. The above distribution is only tentative and may change later. The instructor will announce any changes.

Assignment Submission Policy

Assignments must be turned in by the assigned deadline. Late assignments will not be accepted. Rare exceptions may be made by the instructor only under extenuating circumstances and in accordance with the university policies.

Course Home-page

A variety of materials will be made available on the EC Class Home-page at http://www.cs.uga.edu/~khaled/ECcourse/, including handouts, lecture notes and assignments. Announcements may be posted between class meetings. You are responsible for being aware of whatever information is posted there.

Lecture Notes

Copies of some of Dr. Rasheed's lecture notes will be available at the bottom of the class home page. Not all the lectures will have electronic notes though and the students should be prepared to take notes inside the lecture at any time.

Textbook in Bookstore

·  "Introduction to Evolutionary Computing", Eiben and Smith. Springer-Verlag, New York, 2003. (Required)

Additional Books

·  "Genetic Algorithms in Search, Optimization, and Machine Learning", David Goldberg. Addison-Wesley, 1989.

·  "An Introduction to Genetic Algorithms", Melanie Mitchell. MIT Press, 1996.

·  "Genetic Algorithms + Data Structures = Evolution Programs", Zbigniew Michalewicz. Springer-Verlag, New York,1996.

·  "Evolutionary Computation", D. Dumitrescu et al. CRC Press, 2000.

·  "Evolutionary Computation 1", Thomas Back et al. IOP Publishing, 2000.

Announcements:

·  10-13-2004: The projected grades and all scores are posted HERE. Please make sure that all your scores are properly recorded. If your projected grade is C or lower, I strongly advise you to come and see me tomorrow or Friday. I will be available in my office tomorrow from 2 to 3 p.m. in additional to my office hours on Friday.

Papers:

Assignments:

·  Homework 1

·  Homework 2

·  Homework 3

Lecture Notes:

·  Introduction,Compressed

 

·  Chapter 1

·  Chapter 2

·  Chapter 3

·  GADO,PDF

·  Chapter 4

·  Chapter 5

·  Chapter 6