It is not true that the number of solutions necessarily decreases during the selection phase (if by solutions you mean the number of individuals in the population). The number of solutions is usually constant, i.e., you can start with $N$ individuals, then, every iteration (or generation), you can e.g. select two individuals from the population (typically, the fittest ones, but you can have some more sophisticated selection criteria), then you merge them to create two new individuals (i.e. crossover), which will then replace (with a certain probability) the two least fit individuals from the current population, so the population's size remains constant.
If you are talking about reaching a local minimum, i.e. none of the solutions in the population are "good enough", then, as someone has already suggested, there are potentially multiple ways to address this issue, such as
- increase the population size
- run the genetic algorithm for a longer time (if you have the resources)
- change your genetic operators (i.e. the mutation and crossover) so that to introduce more diversity
- tweak the replacement, mutation, and crossover rates
- change your selection strategy (there are many selection strategies)
- make sure that the representation of the solutions is suitable (e.g. once, by mistake, I was using an array of integers rather than floating-point numbers, so I couldn't ever find the correct solution, which was an array of floating-point numbers)
- use something like novelty search
The correct approach will probably depend on the context.