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.

The source code was written by Genevieve Hayes and is available on GitHub.

User Guide

• Overview
  • Project Background
  • Main Features
  • Installation
  • Licensing, Authors, Acknowledgements
• Tutorial - Getting Started
  • What is an Optimization Problem?
  • Why use Randomized Optimization?
  • Solving Optimization Problems with mlrose
  • Define a Fitness Function Object
  • Define an Optimization Problem Object
  • Select and Run a Randomized Optimization Algorithm
  • Summary
  • References
• Tutorial - Travelling Saleperson Problems
  • What is a Travelling Salesperson Problem?
  • Solving TSPs with mlrose
  • Define a Fitness Function Object
  • Define an Optimization Problem Object
  • Select and Run a Randomized Optimization Algorithm
  • Summary
• Tutorial - Machine Learning Weight Optimization Problems
  • What is a Machine Learning Weight Optimization Problem?
  • Solving Machine Learning Weight Optimization Problems with mlrose
  • Data Pre-Processing
  • Neural Networks
  • Linear and Logistic Regression Models
  • Summary

API Reference

• Algorithms
• Decay Schedules
• Optimization Problem Types
• Fitness Functions
• Machine Learning Weight Optimization