Artificial Intelligence (AI) Mastering Development

How to avoid running out of solutions in genetic algorithm due to selection?

The genetic algorithm consist of 5 phases of which 4 repeat:

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

In the selection phase the number of solutions decreases. How is it avoided to run out of the population before reaching a suitable solution?

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