Genetic algorithms in search, optimization, and machine learning

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Introductory lecture from award-winning UIUC course on genetic algorithms.

Introductory lecture from award-winning UIUC course on genetic algorithms.

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  • 1. Genetic Algorithms in Search, Optimization & Machine Learning Lecture 1 David E. Goldberg Department of Industrial & Enterprise Systems Engineering University of Illinois at Urbana-Champaign Urbana, Illinois 61801 [email_address] © 2006 University of Illinois Board of Trustees, All Rights Reserved
  • 2. Welcome to Genetic Algorithms
    • Genetic Algorithms in Search, Optimization, and Machine Learning.
    • 44 lectures recorded in Studio 403B2 in historic Engineering Hall at the University of Illinois at Urbana-Champaign.
    • An introduction to one of the most exciting fields in engineering and computer science today.
    • Used across the range of human endeavor.
  • 3. Today’s Agenda
    • Administrivia: Review course outline and procedures.
    • Establish goals for the course: Yours and mine.
    • Begin a gentle introduction to genetic algorithms.
  • 4. Syllabus
    • Reading: Course text and papers
    • Homework: Problems & quicky computer assignments
    • Topics
      • Intro, Simple GA code
      • Applicable theory
      • Other Operators, Coding-Constraints
      • Parade of Applications
      • RCGA/ES, GP, GBML
      • Competent GAs, Efficiency
  • 5. Administrative Info
    • Web: http://www.engr.uiuc.edu/OCEE/webcourses/ge531/
    • TA: See web site for information
    • Webboard: See e-mail instructions
    • Office hours: See syllabus online
    • Email: deg@uiuc.edu
    • Office & phone: See campus directory.
    • IlliGAL: http://www-illigal.ge.uiuc.edu/
  • 6. Academic Info
    • Homework/Project/Final: 10-40-50
    • Term project on topic of your choosing
    • Computer language of your choice
    • Final: In-class and take-home portion
    • Traditional scale used
    • One final word: late assignments
  • 7. Cousin Bill
  • 8. Goals: Yours & Mine
    • Why are you here? What prompted you to take the course?
    • What would you like to get out the course?
    • Send short (less than 100 word) goal statement to me by next class period (send as ordinary text to [email_address] ).
    • Mine
      • I want you to be able to read the literature.
      • Apply GAs & evolutionary computation to problems.
      • Capable of original research in the field.
  • 9. A Gentle Introduction
    • What is a GA?
    • Motivation from nature, motivation from artificial search.
    • A brief history.
    • How GAs are different?
    • Mechanics and power of a simple GA.
    • GA terminology and the connection to the real thing.
  • 10. What is a Genetic Algorithm (GA)?
    • A genetic algorithm is an adaptation procedure based on the mechanics of natural genetics and natural selection.
    • GAs have 2 essential components:
      • Survival of the fittest
      • Variation
  • 11. Different Flavors of GA
    • Will use the term genetic algorithm generically. Why? Objectively most popular term.
    • Google hits:
      • “ Genetic algorithm”: 3.24M
      • “ Evolutionary computation”: 1.23M
      • “ Genetic programming”: 1.01M
      • “ Evolutionary programming”: 242k
      • “ Evolution strategy”: 110k
    • GA represents 56% of total.
    • Names as historical “schools” of thought.
    • Distinctions less important today.
  • 12. Nature as Problem Solver
    • Beauty-of-nature argument
    • How Life Learned to Live (Tributsch, 1982, MIT Press, Published originally as Wie das Leben leben lernte. Physikalische Technik in der Natur )
    • Example: Nature as structural engineer (p. 30)
    • stem bamboo insect trachea
  • 13. The Picture’s Pretty Bleak, Gentlemen Gary Larson, The Far Side
  • 14. The Range of Life
    • Lichen (symbiosis between alga and fungus) within 400km of S. Pole (-15º C).
    • Alga in hot springs (85ºC).
    • Whales dive to depth of 1000m and stay submerged for 1-2 hours without air.
    • Tuna - 40 knots. Cheetahs - 100 km/hr. Hawks - 200 km/hr.
    • Golden plovers fly 3500 km nonstop across the pacific.
  • 15. The Scale of Life
    • Size comparison of different macroscopic organisms. From Tributsch, H. (1982). How Life Learned to Live . Cambridge, MA: MIT Press (p. 26).
  • 16. Composite Materials
    • Brown Chilean alga uses sandwich structure to achieve stiffness. From Tributsch, H. (1982). How Life Learned to Live . Cambridge, MA: MIT Press (p. 37).
  • 17. Natural Tools
    • from Tributsch, H. (1982). How Life Learned to Live . Cambridge, MA: MIT Press (p. 43).
  • 18. Beavers Control Environment
    • from Tributsch, H. (1982). How Life Learned to Live . Cambridge, MA: MIT Press (pp. 18-19).
  • 19. Termite Roundabouts from Tributsch, H. (1982). How Life Learned to Live . Cambridge, MA: MIT Press (p. 43).
  • 20. Moths: Stealth Technology
    • from Tributsch, H. (1982). How Life Learned to Live . Cambridge, MA: MIT Press (p. 123).
  • 21. Dive, Dive, Dive
    • Species of seaweed uses air bladders to control sunlight received. From Tributsch, H. (1982). How Life Learned to Live . Cambridge, MA: MIT Press (p. 61).
  • 22. Even Surface Tension from Tributsch, H. (1982). How Life Learned to Live . Cambridge, MA: MIT Press (p. 178).
  • 23. Surface Tension Locomotion
    • Differences in surface tension propel the rove beetle. From Tributsch, H. (1982). How Life Learned to Live . Cambridge, MA: MIT Press (p. 180).
  • 24. Inspiration from Nature
    • Bionics (Bionik in German) or Biomimicry as current craze.
    • Use biological organisms as inspiration for design of artificial systems.
    • Ingo Rechenberg at the Technical University of Berlin a longtime practitioner.
    • GAs/EC a form of bionics or biomimicry.
    • Genetic algorithms and evolutionary computation as
    Ingo Rechenberg
  • 25. Swim of the Penguins: Rechenberg Live Drop Tank
  • 26. And Then There is Homo Sapiens Gary Larson, The Far Side
  • 27. Produced by the College of Engineering University of Illinois at Urbana-Champaign © 2006 University of Illinois Board of Trustees, All Rights Reserved Genetic Algorithms in Search, Optimization & Machine Learning