COMPE 564/ MODES 662 Natural Computing

Transcription

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

2 COMPE 564 / MODES 662 Natural Computing Instructors : Murat KARAKAYA [email protected] Office : Z-14 Lecture : Wednesday 2031 Office Hour : Wednesday 14:00-14:30 Teaching Asst.: TBD TBD Office : TBD Course Web page is on Moodle: Check your registration!

3 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.

4 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.

5 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:

6 Grading (Tentative) Presentations?% Reports?% Demo?% Midterms?% Final Exam?% Passing grade DD >= 60 FD<=59! No bell curve! Catalog will apply

7 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.

8 WEEKLY SCHEDULE AND PRE-STUDY PAGES 1. Week Introduction to Natural Computing Ch.1 INSTRUCTOR 2. Week Introduction to Natural Computing (Self Study) Ch.2 INSTRUCTOR Problem Solving by Search (Hill Climbing; Simulated Annealing) 3. Week Presentations: Genetic Algorithms Artificial Neural Chapter3 BY STUDENTS Source 1 Networks 4. Week Presentations: Artificial Neural Networks Artificial Bee Chapter 4 BY STUDENTS Source2 Colony Optimization 5. Week Presentations: Ant Colony Optimization Particle Swarm Chapter 5 BY STUDENTS Source 3 Optimization 6. Week Optimization Problem Appendix B BY STUDENTS 7. Week Natural Computing Solution Designs for Selected BY STUDENTS Optimization Problems 8. Week Implementation of Natural Computing Solution BY STUDENTS 9. Week Implementation of Natural Computing Solution BY STUDENTS 10. Week Implementation of Natural Computing Solution BY STUDENTS 11. Week Demo and Presentations of the solution BY STUDENTS 12. Week Demo and Presentations of the solution BY STUDENTS 13. Week Demo and Presentations of the solution BY STUDENTS 14. Week Final Report Sunmissions and Presentation BY STUDENTS 15. Week Final Exam

9 GA Literature Survey Presentation Halil Savuran W3 NeuralComp Kerem Yücel W3 Gürsel Karaçor W4 ABC Arda Sezen W4 ACO Emre Tuner W5 Particle Swarm Hamdi Demirel W5 Schedule

10 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 will present, teach & report topic/work assigned to you. You will find an optimization problem, suggest a solution and code your solution. Finally; you are expected to write a paper & submit to a conference You are supposed to be good at Coding (C++, JAVA) - Algorithms Linear Programming - Data Structures Report writing & presenting - Self-motivated

11 Any Questions?