GSKhopt 0.0.6

GSK algorithm for efficient hyperparameter optimization

This an implementation of Gaining-sharing knowledge algorithm (GSK) in python. GSK is a nature inspired algorithm for solving real parameter optimization problems. GSK has two main stages the junior and senior phases each has a different mutation, the dimensions (or parameters) are changed by the mutations of both the junior and senior phases at the same time. GSK is a reliable and stable optimization algorithm. The repository also includes a visualization module for visualizing GSK runs. The code have been tested on CEC 2017 benchmark functions. Two version of GSK the BasicGSK and BasicGSKLSPR (with linear propulation reduction).

Usage

just type

$ python run.py

❤️  How to use GSK as a solver

solver = BasicGSKLPSR(k=10,kf=0.5,kr=0.9,p=0.1)
best , best_fit = solver.run(obj_func, dim, 100, [-100]*dim, [100]*dim)

you can also use the get_statstics functions and Viz after the run

vis = Viz(cec17_test_func,-100,100,dim,1)
best_hist,pop_hist = solver.getstatistics()
best_hist = np.array(best_hist)
best_hist = np.vstack((best_hist))
best_hist = best_hist.reshape((best_hist.shape[0],dim))
vis.set(dim,func+1,best_hist,pop_hist)
vis.build_plot()

There is also an example on using GSK for linear regression using scikit-learn

📫  We would love to hear from you

If you have any comments or questions just email [e-mail] We intend to realse a pip package soon with more examples. More work is done to GSK as a solver for different optimization problems.

✅  Requirements

**python 2.7 or higher **matplotlib (for visualization) **CSVDataFrame (or any other package for saving results)