Genetic Algorithms

This is a group of algorithms that aim at solving Optimization problems with ideas borrowed from biology (evolutions)

General idea:

  • start with a population of potential solutions
  • score each solution
  • select the best (only the best "survive")
  • perform some random mutations (permutations, etc)
  • combine the solutions in hope to get a better one (cross-over)
  • repeat for several generations


This model is successfully applied in many domains:


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