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Using scipy's genetic algorithm for initial parameter estimation in non-linear optimization
I see you are writing Python code using scipy.optimize.curve_fit() which has default starting parameter values of all 1.0 for the Levenber-Marquardt solver. These initial parameter values are not always optimal, and for more complex equations usually cause the algorithm to stop in a local error minimum. For this reason the authors of scipy have added a genetic algorithm for initial parameter estimation for use with their non-linear solvers, the module is named scipy.optimize.differential_evolution and uses the Latin Hypercube algorithm to ensure a thorough search of parameter space.
I have used scipy's Differential Evolution genetic algorithm to determine initial parameters for fitting a double Lorentzian peak equation to Raman spectroscopy of carbon nanotubes and found that the results were excellent. The GitHub project, with a test spectroscopy data file, is:
https://github.com/zunzun/RamanSpectroscopyFit
If you have any questions, please let me know. My background is in nuclear engineering and industrial radiation physics, and I love Python, so I will be glad to help.
James Phillips
Clean up library import
The library Import is not clean. Multiple imports are overlapping.
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