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10-15 511 genetic algorithms and machine learning (alan nochenson)

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10-15 511 genetic algorithms and machine learning (alan nochenson)

  1. 1. Alan NochensonIST 511Oct 15, 2012
  2. 2.  Branch of AI Geared towards taking input and yielding patterns or predictions Use data to capture uncertainty in underlying probability distributions
  3. 3.  1959 – Arthur Samuel: "Field of study that gives computers the ability to learn without being explicitly programmed". Machine learning != data mining
  4. 4.  Decision trees Association rule learning Artificial neural networks Genetic programming Inductive logic programming Support vector machines Clustering Bayesian networks Reinforcement learning Representation learning Sparse dictionary learning
  5. 5.  Machine learning technique Used to optimize a program’s ability to perform some kind of task Inspired by biological evolution (i.e. Darwinian evolution)
  6. 6.  Explicit + implicit parallelism Gracefulness Knowledge about evolutionary processes (brain processes are harder)
  7. 7.  Natural evolution is too slow  Silly argument – generations are much quicker in computers than biology and so many generations have given great process Waddington (1967) says GP algorithms are too simple to get to the same type of complexity as real evolution  Answer: GP works for efficiency in a wide range of problems
  8. 8.  E.g.
  9. 9.  Initialization Selection Crossover (breeding) Mutation Termination
  10. 10.  Let us try to find an algorithm, where the operators are + and – to get to a target number, which we call x. We will restrict ourselves to 5 “spots” and single digit numbers E.g. Let x=10  Solutions would be: ▪ 0+2+8, 1+9-0 , 8+3-1, …
  11. 11.  Create a population of individuals  1+3-4  ++23-  0+4+1  983-2  …
  12. 12.  How far off of our target are we?  1+3-4 (fitness=10-0=0)  ++23- (fitness=-100 since invalid)  0+4+1 (fitness = 10-5=5)  983-2 = 3-2 (fitness = 1)  …
  13. 13.  Select the most fit individuals to breed (or some other algorithm)  1+3-4 (fitness=0)  ++23- (fitness=-100 since invalid)  0+4+1 (fitness = 5)  983-2 = 3-2 = 0+3-2 (fitness = 1)  …
  14. 14.  Crossover (take part from each)  0+4+1  0+3-2  => 0+3+1 = 4 (fitness = 4) Mutate with some probability (change one number of operator Put new individual into population Remove least fit individual (the invalid one)
  15. 15.  1+3-4 = 0 ++23- = -100 (gets removed) 0+4+1 = 5 0+3-2 = 1 0+3+1 = 4
  16. 16.  Keep going until you have a perfectly fit individual Sometimes the trait you need isn’t in the population  That’s why mutation exists Applications are much wider than this, and can generate real programming language code
  17. 17.  http://en.wikipedia.org/wiki/Machine_learnin g http://en.wikipedia.org/wiki/Genetic_progra mming D. E. Goldberg and J. H. Holland, “Genetic Algorithms and Machine Learning,” Machine Learning, vol. 3, no. 2, pp. 95–99, 1988.

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