Genetic Algorithm

Biological Inspiration

  1. Initial population
  2. Fitness function
  3. Selection
  4. Crossover
  5. Mutation

Initial Population

Initial Population

Fitness Function

  • The fitness function should be sufficiently fast to compute.
  • It must quantitatively measure the fitness of a given solution is or how fit individuals can be produced from the given solution.

Selection

  1. Roullete Wheel Selection
  2. Stochastic Universal Sampling (SUS)
  3. Tournament Selection
  4. Rank Selection
  5. Random Selection

Cross Over

Cross Over
  1. One-Point Crossover
  2. Multi-Point Crossover
  3. Uniform Crossover
  4. Davis’ Order Crossover (OX1)
  5. Whole Arithmetic Recombination Crossover

Mutation

Mutation
  1. BitFlip Mutation
  2. Random Resetting
  3. Swap Mutation
  4. Scramble Mutation
  5. Inversion Mutation

Pseudocode

START
Generate the initial population
Compute fitness
REPEAT
Selection
Crossover
Mutation
Compute fitness
UNTIL population has converged
STOP

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Product Engineer @ GO-JEK Tech | History and Literature Enthusiast |IIITIAN | Nerd | Music Lover |

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Anshul vyas

Anshul vyas

Product Engineer @ GO-JEK Tech | History and Literature Enthusiast |IIITIAN | Nerd | Music Lover |

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