For the mutation probability, the definition I found is "Mutation probability (or ratio) is basically a measure of the likeness that random elements of your chromosome will be flipped into something else". (What is Crossover Probability & Mutation Probability in Genetic Algorithm or Genetic Programming?) This is clear if we mutate a gene in a chromosome randomly. But what if we use other techniques? For example, the inversion mutation. How does the mutation probability work here? Or does mutation probability actually apply to the entire chromosome instead of per gene?
For the crossover probability, does this also apply to the entire chromosome?
I am confused because I am reading the Schemata theorem; Wikipedia defines the probability of disruption 
based on the crossover probability and the mutation probability. This doesn't make sense to me if those probabilities apply to an entire chromosome instead of an individual gene. Since, in this case, with the same mutation probability, apparently, the inversion mutation(apply to several genes) is more likely to disrupt the schemata compared to the random mutation(apply to only one gene).
Could someone explain this? Thank you very much!