mlrose-ky: Machine Learning, Randomized Optimization and SEarch#

mlrose-ky 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.

User Guide#

• Project Background
• Main Features
• Project Improvements over mlrose-hiive.
• Installation
• Licensing, Authors, Acknowledgements

Tutorial 1 - Getting Started#

• What is an Optimization Problem?
• Why use Randomized Optimization?
• Solving Optimization Problems with mlrose-ky
• Define a Fitness Function Object
• Define an Optimization Problem Object
• Select and Run a Randomized Optimization Algorithm
• Summary
• References

Tutorial 2 - Travelling Saleperson Problems#

• What is a Travelling Salesperson Problem?
• Solving TSPs with mlrose-ky
• Define a Fitness Function Object
• Define an Optimization Problem Object
• Select and Run a Randomized Optimization Algorithm
• Summary

Tutorial 3 - ML Weight Optimization Problems#

• What is a Machine Learning Weight Optimization Problem?
• Solving Machine Learning Weight Optimization Problems with mlrose-ky
• Data Pre-Processing
• Neural Networks
• Linear and Logistic Regression Models
• Summary

Tutorial 4 - Using Runners#

• An example with RHC runners
• Use runners to make your own custom wrapper

API Reference#

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