Usage Guide

Welcome to the usage guide for the Ensemble Genetic Algorithm project.

This project provides a robust framework for evolving machine learning ensembles for binary classification problems using genetic algorithms. It’s designed to be extensible, highly configurable, and flexible, allowing users to customize base learners, genetic algorithm hyperparameters, and feature space exploration.

Key Features

  • Genetic Algorithm Optimization: Utilizes a genetic algorithm to search for optimal ensembles of machine learning classifiers.

  • Extensible Base Learners: Easily integrate various machine learning algorithms as base learners.

  • Configurable Hyperparameters: Fine-tune genetic algorithm parameters (e.g., population size, generations) and base learner configurations.

  • Feature Space Exploration: Supports grid search over feature spaces and feature transformations.

  • GPU Acceleration: Optional GPU support for PyTorch-based models.

Getting Started

To get started with the project, please refer to the main README.md for installation instructions. This documentation provides detailed guides on using the framework.

Documentation Sections

This site contains detailed information on all aspects of the project. Use the navigation pane on the left to explore topics such as:

  • Data Preparation: How to format your input data.

  • Architecture: A high-level overview of the project’s components.

  • Example Notebook: A walkthrough of a complete experiment.

  • Interpreting Results: How to understand the plots and outputs.

  • API Reference: Auto-generated documentation for the source code.