COMPE 564/ MODES 662 Natural Computing 2013 Fall Murat KARAKAYA Department of Computer Engineering

COMPE 564 / MODES 662 Natural Computing Instructors : Murat KARAKAYA Office : Z-14 Lecture : Wednesday 2031 OfficeHour : Wednesday 14:00-14:30 Teaching Asst.: 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 Other Sources : 1.S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, Prentice-Hall, 2003, ISBN: J. Hertz, A. Krogh and R.G. Palmer, Introduction to the Theory of Neural Computation, Addison-Wesley Publishing Company, 1991, ISBN: M. Dorigo and T. Stützle, Ant Colony Optimization, MIT Press, ISBN: Artificial Intelligence, Patrick H. Winston, Addison-Wesley, ISBN:

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.

WEEKLY SCHEDULE AND PRE-STUDY PAGES 1. WeekIntroduction to Natural ComputingCh.1 2. WeekIntroduction to Natural Computing (Self Study) Problem Solving by Search (Hill Climbing; Simulated Annealing) Ch.2 3. WeekPresentations: Genetic Algorithms Artificial Neural Networks Chapter3 & Source #1 4. WeekPresentations: Artificial Neural Networks Artificial Bee Colony Optimization Chapter & Source #2 5. WeekPresentations: Ant Colony Optimization Particle Swarm Optimization Chapter 5 (Course Book) and Source #3 6. WeekOptimization ProblemAppendix B 7. WeekNatural Computing Solution Designs for Selected Optimization Problems 8. WeekImplementation of Natural Computing Solution 9. WeekImplementation of Natural Computing Solution 10. WeekImplementation of Natural Computing Solution 11. WeekDemo and Presentations of the solution 12. WeekDemo and Presentations of the solution 13. WeekDemo and Presentations of the solution 14. WeekFinal Report Sunmissions and Presentation 15. WeekFinal Exam 16. WeekFinal Exam

Literature Survey Presentation Schedule GA –Halil SavuranW3 NeuralComp –Kerem YücelW3 –Kaled AlhaddatW4 ABC –Arda SezenW4 ACO –Emre TunerW5 Particle Swarm –Hamdi DemirelW5

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

Any Questions?