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: Initial population (initially) Fitness function Selection Crossover 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?