Real-valued genetic algorithm offspring out-of-bounds
Anything is acceptable.  These are all heuristics; all that matters is results (i.e., whether it helps you find a good solution to the problem, and how quickly it does so).

One standard method is to "clip" all values to be within the bounds.  In other words, if the value is smaller than 0, you replace it with 0; if it is larger than 10, you replace it with 10.  This ensures that the value will be within bounds.

There are other methods as well.  For instance, you could let $y = 10 \sigma(x)$ where $\sigma(x) = 1/(1+e^{-x})$.  You apply genetic programming to the values $x$ (so you do mutation and crossover operations on the $x$ values), but then you calculate $y$ and use that as the actual output.  Note that $x$ can be any real number ($-\infty &lt; x &lt; \infty$) and the definition of $y$ will ensure that $0 \le y \le 10$.