Posters

    An Efficient Global Optimization Python Module based on Generalized Simulating Annealing: PyGenSA

    Cano, S.; Gubian, S.; Xiang, Y.; Martin, F.

    Conference date
    Jul 24, 2016
    Conference name
    EuroPython 2016
    Link to more information
    Summary

    Many problems in statistics, finance, biology, pharmacology, physics, mathematics, economics, and chemistry involve the determination of the global minimum of multi dimensional functions. Python modules from SciPy and PyPI for the implementation of different stochastic methods (i.e.: pyEvolve, SciPyoptimize) have been developed and successfully used in the Python scientific community. Based onTsallis 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, basin hopping (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. 

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