CSCI 4560/6560 Evolutionary Computation and Its Applications
CSCI 4560/6560 Evolutionary Computation and Its Applications
Fall 2009: Tuesdays and Thursdays 3:30pm - 4:45pm, Wednesdays 3:35pm -
4:25pm, Geog-Geol 200B
Instructor: Prof. Khaled
Rasheed
Telephone: 542-3444
Office Hours: Wednesday: 4:35-6:00pm & Thursday: 12:30-1:30pm or by email
appointment
Office Location: Room 219B, Boyd GSRC
Email: khaled@cs.uga.edu
Teaching Assistant: Liang Wang
Office Hours: Tuesday: 11am-noon
Office Location: Room 301A, Boyd GSRC
Email: kavenvih@uga.edu
Objectives:
To provide a broad introduction to the field of Genetic Algorithms 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 including
Genetic Programming, Evolution Strategies, Evolutionary Programming
and Classifier Systems. Evolutionary Computation applications in
science and Engineering. Other nature-inspired global optimization techniques.
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 academic work must meet the standards contained in
"A Culture of Honesty." Students are responsible for informing
themselves about those standards before performing any academic
work. 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.
"Evolutionary Computation, A "Unified Approach", K. DeJong. MIT
Press, 2006.
"An empirical study on GAs without parameters" Thomas Back et
al., 2000. [Ananta Palani][10-21]{download}
"A Genetic Programming Approach to Automated Software Repair"
Stephanie Forrest et al., 2009. [Brett Meyer][10-28]{download}
"Performance Scaling of Multi-objective Evolutionary Algorithms"
V. Khare et al., 2003. [Soumya][11-4]{download}
"Differential Evolution for Discrete Optimization: An
Experimental Study on Combinatorial Auction Problems" Jingqiao Zhang,
et al., 2008. [Matthew Tanner][11-10]{download}
"Multiplicative Approximations and the Hypervolume Indicator"
Tobias Friedrich, et al., 2009. [Bernhard][11-17]{download}
"BOA: The Bayesian optimization algorithm". Martin Pelikan et
al.,1999. [Yassin Nachite][11-18] {download}
"String- and Permutation-Coded Genetic Algorithms for the Static
Weapon-Target Assignment Problem" Julstrom, 2009. [Nithya][11-19] {download}
"Using Evolutionary Techniques to Hunt for Snakes and Coils"
[David][?] {download}
"Immunity by Design: An Artificial Immune System" Steven
A. Hofmeyr and Stephanie Forrest,1999.[?][?] {download}
"The generation of form using an evolutionary approach"
Mike Rosenman,1997.[?][?]{download}
"Multiobjective optimization using cooperative coevolution"
K. Maneeratana et al., 2004. [?][?]{download}
"Ant Colony Optimization for dynamic Traveling Salesman Problems"
Carlos A. Silva and Thomas A. Runkler, 2000. [?][?]{download}
Chapter 11The course syllabus is a general plan for the course; deviations
announced to the class by the instructor may be necessary.
Last modified: November 5, 2009.
Khaled Rasheed
(khaled (at) cs.uga.edu)