TransOPT is a modular, data-centric platform for developing, benchmarking, and applying transfer learning for Bayesian optimization (TLBO) algorithms. It empowers researchers and developers to build custom optimization solutions by leveraging historical data for improved efficiency, offering both a web UI and a command-line interface for flexible deployment.

How It Works

TransOPT utilizes a modular, building-block approach to construct custom TLBO algorithms. It supports leveraging historical data from previous optimization tasks to inform new ones, aiming to reduce the need to start from scratch. The system is designed to facilitate the development and comparison of various TLBO methods, bridging the gap between theoretical advancements and practical application.

Quick Start & Requirements

• Installation: Clone the repository, install backend dependencies (python setup.py install), and frontend dependencies (cd webui && npm install).
• Prerequisites: Python 3.10+, Node.js 17.9.1+, npm 8.11.0+.
• Running: Start the backend agent (python transopt/agent/app.py), then launch the web UI (cd webui && npm start) or use the command-line interface (python transopt/agent/run_cli.py).
• Documentation: TransOPT Docs

Highlighted Details

• Supports over 1000 benchmark problems across diverse domains.
• Enables building custom optimization algorithms by combining components.
• Facilitates leveraging historical data for more efficient optimization.
• Offers an intuitive web UI for experiment design and real-time monitoring.

Maintenance & Community

The project is associated with COLA-Laboratory. Further community or maintenance details are not explicitly provided in the README.

Licensing & Compatibility

The repository does not explicitly state a license in the provided README. This requires further investigation for commercial use or closed-source linking.

Limitations & Caveats

The README does not specify any limitations or known caveats. The project appears to be actively developed, with a recent citation in 2024.