Understanding genetic algorithms
A genetic algorithm is an algorithm based on biological evolution, on how nature evolved. It does exactly that, evolve the algorithm so that "it" finds the best solution to the problem at hand. You can use a genetic algorithm to find a solution to a problem which you don't know the answer, you know the answer but want to know a different one, or you are just lazy. The common steps for a genetic algorithm are:


Generate a random population of elements
Evaluate fitness of each element (how good are they against the
solution)
Take the best elements for the next generation
Generate child elements using the above elements 
Mutate (randomly) each child. This can also be done before step 4 with parents.
Repeat from step 2 for n number of generations until the solution is found, the maximum number of generations have been reached or the fitness is not changing anymore (local minima).


Two basic problems with GAs:


To get trapped in a local minima (or local maximum depending on the point of view). This means that you might find a good answer (or not even a good one) but it will never reach the best answer because fitness values are not changing or not getting better. This is fought using Mutation in step 5 to keep diversity in the population so it doesn't get stuck.
They are generally slower than other approaches.


If you can, take a look at this book. It is not free, but worth a look.
Alternatively, take a look at this online book for free, it is a great source and he has his own youtube channel. It's more basic than the other book I recommend, but will help you get started with GAs.

To answer the other part of your question, the GA should be used to find a model, but will not act as a model. For example, if you have a neural network you can train it using the backpropagation method, but you could also train it using a GA. The GA will not use anything of the backpropagation mathematics, it could be used to generate weights in its neurons, and evaluate the answer (last layer). Weights will evolve to get closer to the solution. In this scenario, the model will still be the NN, but you used a different algorithm to find the best NN.

Hope this helps.