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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 : Wednesday 14:30-17:20 @ 2031 • Office Hour : Wednesday 14:00-14:30 • • • • Teaching Asst.: TBD Email : 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, Prentice-Hall, 2003, ISBN: 0-13-790395-2 2. J. Hertz, A. Krogh and R.G. Palmer, Introduction to the Theory of Neural Computation, Addison-Wesley Publishing Company, 1991, ISBN: 0-201-50395-6. 3. M. Dorigo and T. Stützle, Ant Colony Optimization, MIT Press, 2004. ISBN: 0-262-04219-3 4. 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 7. 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 8. Week Natural Computing Solution Designs for Selected Optimization Problems Implementation of Natural Computing Solution 9. Week 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 Ch.2 Chapter3 & Source #1 Chapter & Source #2 Chapter 5 (Course Book) and Source #3 Appendix B Literature Survey Presentation Schedule • GA – Halil Savuran W3 • NeuralComp – Kerem Yücel – Kaled Alhaddat W3 W4 • ABC – Arda Sezen W4 • ACO – Emre Tuner W5 • Particle Swarm – Hamdi Demirel 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 – Linear Programming – Report writing & presenting - Algorithms - Data Structures - Self-motivated Any Questions?