Genetic algorithms in search, optimization, and machine learning

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

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Genetic algorithms in search, optimization, and machine learning

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

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