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Randomized search algorithms applied to optimization problems and neural networks

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Randomized Optimization Assignment - Giovanni Peralto

Code was written in Python 3.x. Python/pip is needed to run the program.

Instructions:

Install dependencies:

  • pip install matplotlib
  • pip install numpy
  • pip install scikit-learn
  • pip install pandas
  • pip install mlrose
  • pip install time
  • pip install tqdm

Create images directories:

  • mkdir images
  • mkdir images/nn
  • mkdir images/optimizatin-problems
  • mkdir images/optimizatin-problems/4-peaks
  • mkdir images/optimizatin-problems/10-queens
  • mkdir images/optimizatin-problems/knapsack

Run instructions:

  • Knapsack: python knapsack.py
  • Four Peaks: python 4-peaks.py
  • 10-Queens: python 10-queens.py
  • Neural Networks: python nn.py (takes a ~30m)
    • GA learning curve takes longest, if you comment it out everything else should run in ~15m

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