Efficient Global Optimization based on Generalized Simulating Annealing: PyGenSA
Many problems in statistics, finance, biology, pharmacology, physics, mathematics, economics, and chemistry involve the determination of the global minimum of multidimensional functions. Python modules from SciPy and PyPI for the implementation of different stochastic methods (i.e.: pyEvolve, SciPy optimize) have been developed and successfully used in the Python scientific community. Based on Tsallis statistics, the PyGenSA python module has been developed for generalized simulated annealing to process complicated non-linear objective functions with a large number of local minima. Testing PyGenSA, basinhopping (SciPy) and differential evolution (SciPy) on many standard test functions used in optimization problems shows that PyGenSA is more reliable in general and more efficient in particular for high dimension problems.
I make research in Protein Structure Prediction, and I developed an evolutionary algorithm called DEEPSAM (Diffusion Equation Evolutionary Programming Simulated Annealing Method), implemented in a combination of Python and Fortran. Your work sounds me very interesting, and may be that we could collaborate somehow. I would like to talk with you when I will be at EuroPython 2017. Please, e-mail me at goldmosh@g.jct.ac.il