-1
$\begingroup$

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?

$\endgroup$
3
  • 1
    $\begingroup$ Just making sure: do you mean "genetic" or "generic" ? $\endgroup$
    – Hilmar
    Commented Nov 22, 2021 at 14:32
  • $\begingroup$ Genetic algorithm $\endgroup$
    – Blobmou
    Commented Nov 22, 2021 at 14:32
  • 1
    $\begingroup$ Thanks for clarifying . Can you add maybe some background? What research have you done so far and why do you think that genetic algorithms are a good fit for this type of problem? Also it would help if you define "similarity" some more. What are the classes of operations/transforms that you consider to be "similar" versus "dissimilar"? $\endgroup$
    – Hilmar
    Commented Nov 22, 2021 at 14:41

1 Answer 1

0
$\begingroup$

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!

$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.