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MKI44: Evolutionary Algorithms

MKI44: Evolutionary Algorithms. Organisation. Teachers. Ida Sprinkhuizen-Kuyper Room B.02.39 E-mail: i.kuyper@donders.ru.nl Phone: 024-3616126 URL: http://www.nici.ru.nl/~idak

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MKI44: Evolutionary Algorithms

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  1. MKI44: Evolutionary Algorithms Organisation MKI44: EAs

  2. Teachers • Ida Sprinkhuizen-KuyperRoom B.02.39E-mail: i.kuyper@donders.ru.nlPhone: 024-3616126URL: http://www.nici.ru.nl/~idak • Pim HaselagerE-mail: w.haselager@donders.ru.nlRoom B.02.40 Phone: 024-3616066URL: http://www.nici.ru.nl/~haselag • Ruud BarthE-mail: rudoros@gmail.com MKI44: EAs

  3. Evolutionary Algorithms • Evolutionary Algorithms • Set up: • Theory • Self study • Presenting summaries • Discussion • Practice • Project using/studying EAs MKI44: EAs

  4. Goals • This course contributes to the following final qualifications of a master AI: • 1: Knowledge and understanding of AI • 4: Knowledge and understanding of different model types • 5: Analysing problems • 6: Research skills • 11: Learning skills MKI44: EAs

  5. Material • Book: A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing, corrected 2nd printing, 2007  • Websites:book + material:http://www.cs.vu.nl/~gusz/ecbook/ecbook.htmlcourse:http://www.ai.ru.nl/aicourses/mki44ECJ:http://www.cs.gmu.edu/~eclab/projects/ecj/ MKI44: EAs

  6. What are Eas? • Stochastic, population-based, general applicable problem-solving algorithms, inspired by natural evolution • Survival of the fittest MKI44: EAs

  7. General scheme MKI44: EAs

  8. Typical EA MKI44: EAs

  9. Problem type 1 : Optimisation • We have a model of our system and seek inputs that give us a specified goal • e.g. • time tables for university, call center, or hospital • design specifications, etc etc MKI44: EAs

  10. Problem type 2: Modelling • We have corresponding sets of inputs & outputs and seek model that delivers correct output for every known input • Evolutionary machine learning MKI44: EAs

  11. Problem type 3: Simulation • We have a given model and wish to know the outputs that arise under different input conditions • Often used to answer “what-if” questions in evolving dynamic environments • e.g. Evolutionary economics, Artificial Life MKI44: EAs

  12. Global schedule • Today (8-9): Introduction • Next week: • Tuesday (15-9): More about evolution (Pim) • Wednesday (16-9): Working with ECJ (Ruud) • Upto 3-11: Studying the book, designing a project • Tuesdays 22-9 till 3-11: short presentations of the chapters and discussion • Wednesdays: Practical work with ECJ • Upto 19-1-2010: Project, guest lectures • 19-1-2010: Presentation/demonstration of the projects MKI44: EAs

  13. Chapters • 22-9 • 29-9 • 29-9 • 6-10 • 6-10 • 13-10 • 13-10 • 20-10 • 20-10 • 27-10 • 27-10 • 3-11 • 3-11: Pim/Ida • Introduction • What is an Evolutionary Algorithm? • Genetic Algorithms • Evolution Strategies • Evolutionary Programming • Genetic Programming • Learning Classifier Systems • Parameter Control in Evolutionary Algorithms • Multi-Modal Problems and Spatial Distribution • Hybridisation with Other Techniques: Memetic Algorithms • Theory • Constraint Handling • Special Forms of Evolution • Working with Evolutionary Algorithms • Summary MKI44: EAs

  14. Organisation • We will randomly distribute the chapters • For presenting a concise summary: 1 or 2 students • For formulating some discussion questions: 2 or 3 students • All students have to study the chapters before the lecture and should be involved in questions and discussions during the lectures • Goal of studying the book is to learn the possibilities of the different forms of Eas, learning how to use the terminology correctly, how to choose important parameters, etc. MKI44: EAs

  15. The project • Groups of 2 or 3 students • Project proposal: deadline 3-11 • Research question • Motivation for EAs • Experimental set up: • Representation • Fitness function • Type(s) of Eas • … MKI44: EAs

  16. Examination • The result of the course is determined by the project • The Project will be judged on • The presentation/demonstration (20%) • Project proposal, design, implementation, originality (40%) • The report (40%) • Motivation of choices (representation, fitness function, type of Eas, …) • Correct use of EA terminology • Statistical analysis of the results MKI44: EAs

  17. Ideas for projects • Aspects of a project: • Task • EA • Task types • Optimizing (scheduling, robot controller, …) • Modeling (datamining, bci, …) • Simulation (mirror neurons, artificial societies, …) MKI44: EAs

  18. Ideas for projects (2) • EAs • Genetic algorithms • Genetic programming • Constraint satisfaction • Coevolution • … MKI44: EAs

  19. Examples • WEIRD webpagehttp://www.ru.nl/ai/onderwijs/stages_scripties/weird/ • Student projects: Many examples of projects • Demo of an EA for evolving a robot controller for a box-pushing task MKI44: EAs

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