Genetic Algorithm in Optimization of Events
To start, let's make sure I understand your problem.
You have a set of sample data, each element containing a time series of a binary variable (we'll call it V). When V is set to True, a function (A, B, or C) is applied which returns V to it's False state. You would like to apply a genetic algorithm to determine which function (or solution) will return V to False in the least amount of time.
If this is the case, I would stay away from GAs. GAs are typically used for some kind of function optimization / tuning. In general, the underlying assumption is that what you permute is under your control during the algorithm's application (i.e., you are modifying parameters used by the algorithm that are independent of the input data). In your case, my impression is that you just want to find out which of your (I assume) static functions perform best in a wide variety of cases. If you don't feel your current dataset provides a decent approximation of your true input distribution, you can always sample from it and permute the values to see what happens; however, this would not be a GA.
Having said all of this, I could be wrong. If anyone has used GAs in verification like this, please let me know. I'd certainly be interested in learning about it.
I'm  a data analysis student and I'm starting to explore Genetic Algorithms at the moment. I'm trying to solve a problem with GA but I'm not sure about the formulation of the problem. 
Basically I have a state of a variable being 0 or 1 (0 it's in the normal range of values, 1 is in a critical state). When the state is 1 I can apply 3 solutions (let's consider Solution A, B and C) and for each solution I know the time that the solution was applied and the time where the state of the variable goes to 0. 
So I have for the problem a set of data that have a critical event at 1, the solution applied and the time interval (in minutes) from the critical event to the application of the solution, and the time interval (in minutes) from the application of the solution until the event goes to 0. 
I want with a genetic algorithm to know which is the best solution for a critical event and the fastest one. And if it is possible to rank the solutions acquired so if in the future on solution can't be applied I can always apply the second best for example.
I'm thinking of developing the solution in Python since I'm new to GA.
Edit: Specifying the problem (responding to AMack)
Yes is more a less that but with some nuances. For example the function A can be more suitable to make the variable go to F but because exist other problems with the variable are applied more than one solution. So on the data that i receive for an event of V, sometimes can be applied 3 ou 4 functions but only 1 or 2 of them are specialized for the problem that i want to analyze. My objetive is to make a decision support on the solution to use when determined problem appear. But the optimal solution can be more that one because for some event function A acts very fast but in other case of the same event function A don't produce a fast response and function C is better in that case. So in the end i pretend a solution where is indicated what are the best solutions to the problem but not only the fastest because the fastest in the majority of the cases sometimes is not the fastest in the same issue but with a different background.