I am working on an optimized method for measuring the similarity between 2 signals, Is it possible to use a genetic algorithm for finding the correlation among time series?
A genetic algorithm can converge towards a correlation, I see no reason why correlation would be any different than any other function mapping two signals to a real number... in fact, correlation is nice in terms of partial derivatives, so that most methods of optimization would converge on it, if the metric you're using actual has a minimum reached by the function known as "correlation". In cases where the "classic" optimization methods work (simple gradient descent?) it's probably not a good idea to throw genetic algorithms at the problem.
Why you wouldn't just start by using the correlation right away instead of "developing" it in sequence of functions – nobody knows but you. Why use an optimization technique if you know the optimum!