Analyze randomized optimization algorithms in their performance on training a neural network, Continuous Peaks, and the Traveling Salesman Problem
This analysis uses Jupyter Notebook with numpy, pandas, and matplotlib, which is available in a standard Anaconda distribution, as well as the ABAGAIL package which is accessible on github. For the neural network weight optimization, the jupyter notebook can be run. For the two other problem domains, build ABAGAIL by running ant in the terminal and then compile the ContinuousPeaksTest as well as the TravelingSalesmanTest files. This will generate CSV files that will be used in the remainder of the jupyter notebook file.