Standard Parameters for Genetic Algorithms
As you say, there are no universal parameter settings, which means that you won't find a source that cites any such settings. Instead, you find some papers who tune the parameters to their particular problem, and you find many, many others that simply take a "standard" set of parameter settings without much attempt to justify them. In that case, it's not like there's a single authoritative source you can cite -- it's more like bunches of people sort of converge on parameters that seem common to everyone. The best you can really do is to say something like "the parameter values were chosen as being commonly observed in the literature."

Also, it depends not just on the problem, but on the particular structure of your GA. CHC (Eshelman, 1991), for example, is usually used with a population size of 50 (very small by typical standards) and has no conventional mutation operator, but it adds other pieces that make up for it. Genetic programming practitioners often use population sizes in the thousands and use very high mutation rates.

For a "standard GA", if you were to use a population size of 100-200, a mutation rate of 1/L (with L=the length of your encoding, assuming it's discrete), and "enough" generations (more on that in a second), no one would question things too much. However, aside from just citing a random selection of papers, there's not a lot you can point to as justification. Also, some light parameter tuning is almost certainly a better idea if you can afford it at all.

For generation count, the basic rule is to go until you either exhaust the amount of time you have to allot to it or until the algorithm converges, whichever comes first. Of course, these are interdependent. Raising the population size and/or raising the mutation rate will slow convergence, so you still need to tune parameters for the best results. 

If you're doing this for research purposes at least, you should also measure the runtime in fitness function evaluations rather than generations. The reason is that if my algorithm finds a solution in N generations, you can almost certainly find a better solution in N generations if you just multiply the population size by 100 or so. You'll get to do about two orders of magnitude more searching than I could do, so the comparison is unfair. Reporting generations used is an automatic red flag in a paper unless the other GA parameters are controlled to a degree that we can be sure that the comparison is apples to apples. Measuring evaluations is much more tightly correlated to wall-clock time than measuring generations, and should essentially always be preferred.