Can I run a GA to optimize wavelet transform? I am running a wavelet transform (cmor) to estimate damping and frequencies that exists in a signal.cmor has 2 parameters that I can change them to get more accurate results. center frequency(Fc) and bandwidth frequency(Fb). If I construct a signal with few freqs and damping then I can measure the error of my estimation(fig 2). but in actual case I have a signal and I don't know its freqs and dampings so I can't measure the error.so a friend in here suggested me to reconstruct the signal and find error by measuring the difference between the original and reconstructed signal e(t)=|x(t)−x^(t)|. 
so my question is:
Does anyone know a better function to find the error between reconstructed and original signal,rather than e(t)=|x(t)−x^(t)|.
can I use GA to search for Fb and Fc? or do you know a better search method?
Hope this picture shows what I mean, the actual case is last one. others are for explanations

Thanks in advance
You should be able to use GA or any other derivative-free optimization methods to solve your problem [1]. The correct parameter values can be determined using your application. If I'm trying to optimize for compression then I will look into the entropy of the resultant coefficients. Deciding on the actual values for your parameters will depend on how you plan to use them. The correct parameter values are simply the ones that work better.