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).
just type
$ python run.py
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
If you have any comments or questions just email h.nomer@nu.edu.eg We intend to realse a pip package soon with more examples. More work is done to GSK as a solver for different optimization problems.
**python 2.7 or higher **matplotlib (for visualization) **CSVDataFrame (or any other package for saving results)