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  1. 1. Genetic Programming Chapter 6
  2. 2. G P quick overview <ul><li>Developed: USA in the 1990’s </li></ul><ul><li>Early names: J. Koza </li></ul><ul><li>Typically applied to: </li></ul><ul><ul><li>machine learning tasks (prediction, classification…) </li></ul></ul><ul><li>Attributed features: </li></ul><ul><ul><li>competes with neural nets and alike </li></ul></ul><ul><ul><li>needs huge populations (thousands) </li></ul></ul><ul><ul><li>slow </li></ul></ul><ul><li>Special: </li></ul><ul><ul><li>non-linear chromosomes: trees, graphs </li></ul></ul><ul><ul><li>mutation possible but not necessary (disputed!) </li></ul></ul>
  3. 3. GP technical summary tableau Representation Tree structures Recombination Exchange of subtrees Mutation Random change in trees Parent selection Fitness proportional Survivor selection Generational replacement
  4. 4. سلام اسلسفلذتسمه 9 ایذتاا