Transcript Document
COMPE 564/ MODES 662 Natural Computing
2013 Fall
Murat KARAKAYA Department of Computer Engineering
COMPE 564 / MODES 662 Natural Computing
• • •
Instructors Email Office : : :
Murat KARAKAYA [email protected]
Z-14 • •
Lecture Office Hour : :
Wednesday 14:30-17:20 @ 2031 Wednesday 14:00-14:30 • • • •
Teaching Asst.: Email :
TBD TBD
Office :
TBD
Course Web page
is on Moodle: Check your registration!
Objectives & Content Objectives:
• to teach different nature inspired computing techniques; • to gain an insight about how to solve real-life practical computing and optimization problems.
Objectives & Content
• Gain necessary knowledge about nature-inspired computing mechanisms, including
Hill Climbing, Simulated Annealing, Genetic Algorithms, Neural Networks, Swarm Intelligence (e.g.
Ant Colonies, Particle Swarm Optimization) and Artificial Immune Systems.
• Understand and improve the mentioned nature inspired computing techniques • Applying the nature-inspired computing techniques to real-life practical problems • Develop necessary software codes in the nature-inspired computing context.
Text Books and References
Course Book:
1. Leandro Nunes de Castro, Fundamentals of Natural Computing: Basic Concepts, Algorithms and Applications, Chapman & Hall/CRC, 2006, ISBN 1-58488-643-9.
Other Sources
:
1.
S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 2.
Prentice-Hall, 2003, ISBN: 0-13-790395-2 J. Hertz, A. Krogh and R.G. Palmer, Introduction to the Theory of Neural Computation, Addison-Wesley Publishing Company, 1991, 3.
4.
ISBN: 0-201-50395-6.
M. Dorigo and T. Stützle, Ant Colony Optimization, MIT Press, 2004. ISBN: 0-262-04219-3 Artificial Intelligence, Patrick H. Winston, Addison-Wesley, 1992. ISBN: 0-201-533774
Grading
(Tentative)
• Presentations • Reports • Demo • Midterms • Final Exam ?% ?% ?% ?% ?%
– Passing grade DD >=
60 FD<=59!
–
No bell curve! Catalog will apply
Grading Policies
• Missed exams: o
no make-up exam for midterms without approved excuse!
o
no make-up exam for final for any excuse!
• Ethics: o All assignments/projects are to be
your own work
. • Participation: o You are supposed to be active in the class by involving and participating disscusions via asking questions, proposing solutions, explaning your ideas, etc.
1. Week 2. Week 3. Week 4. Week 5. Week 6. Week
WEEKLY SCHEDULE AND PRE-STUDY PAGES
Introduction to Natural Computing Ch.1
Introduction to Natural Computing (Self Study) Problem Solving by Search (Hill Climbing; Simulated Annealing) Presentations: Genetic Algorithms Artificial Neural Networks Presentations: Artificial Neural Networks Artificial Bee Colony Optimization Presentations: Ant Colony Optimization Particle Swarm Optimization Optimization Problem Ch.2
Chapter3 & Source #1 Chapter & Source #2 Chapter 5 (Course Book) and Source #3 Appendix B 7. Week 8. Week 9. Week Natural Computing Solution Designs for Selected Optimization Problems Implementation of Natural Computing Solution Implementation of Natural Computing Solution 10. Week Implementation of Natural Computing Solution 11. Week Demo and Presentations of the solution 12. Week Demo and Presentations of the solution 13. Week Demo and Presentations of the solution 14. Week Final Report Sunmissions and Presentation 15. Week Final Exam 16. Week Final Exam
Literature Survey Presentation Schedule
• GA – Halil Savuran • NeuralComp – Kerem Yücel – Kaled Alhaddat • ABC – Arda Sezen • ACO – Emre Tuner • Particle Swarm – Hamdi Demirel W3 W3 W4 W4 W5 W5
WORK LOAD & EXPECTED SKILLS
Need to
have a copy
of the Text Book You
have to read
the chapters in the book and
research
for the related papers.
You
have to take note
during the lectures or classes.
You will
present, teach
&
report
your topic/worki You will
code
your solution to the selected problem.
Finally; you are expected to
write
a paper &
submit
to a conference You are supposed to
be good at
– Coding - Algorithms – Linear Programming - Data Structures – Report writing & presenting - Self-motivated