mlrose is a Python package for applying some of the most common randomized optimization and search algorithms to a range of different optimization problems, over both discrete- and continuous-valued parameter spaces.

mlrose was initially developed to support students of Georgia Tech's OMSCS/OMSA offering of CS 7641: Machine Learning.

It includes implementations of all randomized optimization algorithms taught in this course, as well as functionality to apply these algorithms to integer-string optimization problems, such as N-Queens and the Knapsack problem; continuous-valued optimization problems, such as the neural network weight problem; and tour optimization problems, such as the Travelling Salesperson problem. It also has the flexibility to solve user-defined optimization problems.

At the time of development, there did not exist a single Python package that collected all of this functionality together in the one location.

• Implementations of: hill climbing, randomized hill climbing, simulated annealing, genetic algorithm and (discrete) MIMIC;
• Solve both maximization and minimization problems;
• Define the algorithm's initial state or start from a random state;
• Define your own simulated annealing decay schedule or use one of three pre-defined, customizable decay schedules: geometric decay, arithmetic decay or exponential decay.

• Solve discrete-value (bit-string and integer-string), continuous-value and tour optimization (travelling salesperson) problems;
• Define your own fitness function for optimization or use a pre-defined function.
• Pre-defined fitness functions exist for solving the: One Max, Flip Flop, Four Peaks, Six Peaks, Continuous Peaks, Knapsack, Travelling Salesperson, N-Queens and Max-K Color optimization problems.

• Optimize the weights of neural networks, linear regression models and logistic regression models using randomized hill climbing, simulated annealing, the genetic algorithm or gradient descent;
• Supports classification and regression neural networks.

mlrose was written in Python 3 and requires NumPy, SciPy and Scikit-Learn (sklearn). Andrew Rollings made modifications to the genetic algorithm to improve performance, including elites and dregs. Additional modifications were made to allow for periodic output.

The version used for CS7641 assignment 2 is available at https://github.com/tmsuidan/mlrose/ and can be installed using pip:

pip install git+https://github.com/tmsuidan/mlrose

The official mlrose documentation can be found here.

A Jupyter notebook containing the examples used in the documentation is also available here.

mlrose was written by Genevieve Hayes and is distributed under the 3-Clause BSD license.

You can cite mlrose in research publications and reports as follows:

• Hayes, G. (2019). mlrose: Machine Learning, Randomized Optimization and SEarch package for Python. https://github.com/gkhayes/mlrose. Accessed: day month year.

BibTeX entry:

@misc{Hayes19,
 author = {Hayes, G},
 title 	= {{mlrose: Machine Learning, Randomized Optimization and SEarch package for Python}},
 year 	= 2019,
 howpublished = {\url{https://github.com/gkhayes/mlrose}},
 note 	= {Accessed: day month year}
}