CSCI 4560/6560 Evolutionary Computation and Its Applications
CSCI 4560/6560 Evolutionary Computation and Its Applications
Fall 2024: Tuesdays and Thursdays 2:20pm - 3:35pm & Mondays 3:00pm -
3:50pm, Boyd GSRC Room 208
Instructor: Prof. Khaled
Rasheed
Telephone: 542-0881
Office Hours: Tuesday 12 pm to 2 pm or by email arrangement
Office Location: Room 518, Boyd GSRC
Email: khaled@uga.edu
Teaching Assistant: Jane Odum
Office hours: Wednesdays: 1:45 PM - 2:45 PM
Office Location: Boyd 307
Email: jane.odum@uga.edu
Attendance Note:
I will do the lecture in person (face to face) in room 208 and all
students are expected to attend unless you are sick or under
quarantine. In such cases please email me and I can work with you by
zoom to catch up. If you are planning to travel in December, make sure
your travel date is after the final exam. I cannot offer early makeup
exams under any circumstances.
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 or CSCI 2725 Data Structures. Students must have a strong
understanding of basic computer algorithms and data structures and at
least one high level programming language.
Topics to be Covered:
Components of an Evolutionary Algorithm, Genetic Algorithms, Evolution
Strategies, Evolutionary Programming, Genetic Programming, Learning
Classifier systems, Parameter Control, Multi-modal Problems,
Multi-objective Evolutionary Optimization, Hybridization and Memetic
Algorithms, Working With Evolutionary Algorithms, Theory of
Evolutionary Computation, 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.)
Course Objectives and Expected Outcomes:
This course presents a survey of topics in evolutionary computation.
At the end of the semester, all students are expected to be able to do
the following:
Formulate a problem as an evolutionary computation
search/optimization by specifying representations, selection and
variation operators.
Write a program or use a package to implement an evolutionary algorithm.
Conduct evolutionary optimization experiments and properly report
and discuss the results.
Effectively present an evolutionary computation article to an audience.
Review and critique evolutionary computation articles.
Reason about the schema theorem and the theory of evolutionary computation.
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.
Graduates: Final Examination 10% And Term Project: 20% (includes
term paper and presentation)
Term projects are required for graduate students and optional for
undergraduates. If any undergraduates choose to do term projects,
their grade distribution will be the same as that of graduate
students. Students may work on their term projects individually or in
groups of up to Four 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 lose 10% for every calendar day. Rare exceptions may
be made by the instructor in case of illness or 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://khaledmrasheed.github.io/ECcourse/. Announcements may be
posted between class meetings. You are responsible for being aware of
whatever information is posted there.
Lecture Notes
Copies of Dr. Rasheed's lecture notes will be available on eLC. 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
"Introduction to Evolutionary Computing", Eiben and
Smith. Springer-Verlag,First Edition Corrected 2nd printing, 2008,
ISBN 10: 3540401849, ISBN 13: 9783540401841. (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.
"Using Interactive Evolutionary Computation (IEC) with Validated
Surrogate Fitness Functions for Redistricting" 2012. [Bahaa,Nathan Mcentire][10-31]
{download}
"Multi-Objective Mathematical Optimization in Assisted Production Planning" 2024. [Stefan Tse, Alexander Darwiche ][11-4]
{download}
"From evolutionary ecosystem simulations to computational models of human behavior" 2020. [Asia Grant,Carson Cooper ][11-5]
{download}
"Reinforced Evolutionary Algorithms for Game Difficulty Control" 2020. [Vincent Liu, Brandon Czech][11-5]
{download}
"An empirical study on GAs without parameters" Thomas Back et
al.,2000. [Adam Brams, Christian Jensen][11-7]{download}
"Selecting Best Investment Opportunities from Stock Portfolios Optimized by a Multiobjective Evolutionary Algorithm" 2015. [Hayden Saunders, Connor Pillsworth][11-7]
{download}
"Evolving an Expert Checkers Playing Program without Using Human
Expertise" Kumar Chellapilla and David Fogel, 2001. [Logan James,Augustin Lorenzo][11-7]
{download}
"An Approach for Team Composition in League of Legends using
Genetic Algorithm" 2019. [Azad Kazemi,Aidan LeCroy][11-11]
{download}
"Evolutionary computation approaches for real offshore wind farm layout: A case study in northern Europe" 2013. [Lisa Schmidt, Victoria Kuch][11-11]
{download}
"AUDIO-GUIDED ALBUM COVER ART GENERATION
WITH GENETIC ALGORITHMS" 2022. [He Yang, Ezi Ononuju][11-12]
{download}
"Feature Selection for Improving Failure Detection in Hard Disk
Drives Using a Genetic Algorithm and Significance Scores"
2020. [Saim Karim, Jovita Chang][11-12]{download}
"Evolving the process of a virtual composer" Csaba Sulyok et
al.,2019. [Jheel, Weixuan][11-14]{download}
"Automatically Designing CNN Architectures Using the Genetic
Algorithm for Image Classification" 2020. [Noah Solomon, Michael Tikhonovsky][11-14]
{download}
"Real-World Evolution Adapts Robot Morphology and Control to
Hardware Limitations" 2018. [Akil Mir,Vansh Arora][11-14]
{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: 10/30/2024.
Khaled Rasheed
(khaled (at) uga.edu)