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Genetic Algorithms

Genetic Algorithms

Solving problems the natural way

Luke Williams

July 16, 2020
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Transcript

  1. Under the hood Candidates Genome Fitness List of candidate solutions

    to the problem. “Genetic” code for each candidate that describes the characteristics that will “evolve” How to determine the “fittest” candidates
  2. Under the hood Clone/mate to build the next generation Find

    the fittest candidate(s) Randomly mutate the genome Build all candidates in generation
  3. In the wild - Flight / train scheduling - Timetabling

    - e.g at a school - Factory floor design - Mechanical engineering - Music production - Financial modelling - Code-breaking - Artificial Intelligence