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mlrose: Machine Learning, Randomized Optimization and SEarch

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.

Project Background

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.

Main Features

Randomized Optimization Algorithms

  • 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.

Problem Types

  • 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.

Machine Learning Weight Optimization

  • 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).

The latest released version is available at the Python package index and can be installed using pip:

pip install mlrose


The official mlrose documentation can be found here.

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

Licensing, Authors, Acknowledgements

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:

BibTeX entry:

 author = {Hayes, G},
 title 	= {{mlrose: Machine Learning, Randomized Optimization and SEarch package for Python}},
 year 	= 2019,
 howpublished = {\url{}},
 note 	= {Accessed: day month year}


Python package for implementing a number of Machine Learning, Randomized Optimization and SEarch algorithms.







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