What are the advantages and disadvantages of using the crossover genetic operator?

For example, we have this problem:

Maximise the function f(X) = X^2 , with 0 ≤ X ≤ 31

Using binary encoding we can represent individuals using 5 bits. After undergoing a selection method, we get to the genetic operators.

For this problem (or any optimisation problem), what are the advantages and disadvantages of the following:

• High or Low crossover rate
• Using 1-Point crossover
• Using multi-point crossover
• Using Uniform crossover

Here's what I came up with so far:

• High crossover rates and multi-point crossover can decrease the quality of parents with good fitness, and produce worse offspring
• Low crossover rates mean the solution will take longer to converge to some optima

It's hard to give a good answer as more information is needed what exactly the 5 bits represent, but I gave it a try:

• A high crossover rate causes the genomes in the next generation to be more random, as there will be more genomes that are a mix of previous generation genomes
• A low crossover rate keeps fit genomes from the previous generation, although it decreases the chance that a very fit genome will be produced by crossover operation
• Uniform crossover will create genomes that will be very different from their parents if their parents are not similar. If its parents are similar, the offspring will be similar to its parents.
• Using 1-point crossover means that offspring genomes will be less diverse, they will be quite similar to their parents.
• Using multi-point crossover is basically a mix between 1-point and uniform, depending on the amount of points.