https://github.com/deepbiolab/self-adaptive-optimization

https://github.com/deepbiolab/self-adaptive-optimization

Last synced: 6 months ago
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• Host: GitHub
• URL: https://github.com/deepbiolab/self-adaptive-optimization
• Owner: deepbiolab
• License: mit
• Created: 2025-02-04T21:07:10.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-02-04T21:10:33.000Z (over 1 year ago)
• Last Synced: 2025-02-04T22:19:51.093Z (over 1 year ago)
• Size: 5.86 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Self-Adaptive Optimization

Self-Adaptive Optimization is an end-to-end adaptive optimization system designed for the biotechnology domain. This project leverages technologies such as Large Language Models (LLMs) and Reinforcement Learning (RL) to automate the generation, evaluation, and optimization of experimental designs. It aims to streamline experimental workflows, reduce manual intervention, and accelerate research and development processes.

## Project Overview

- **Automated Solution Generation**: Generates potential optimization solutions based on user-defined experimental tasks using LLMs.

- **Smart Evaluation and Ranking**: Dynamically evaluates solutions using both virtual scoring models (in silico) and real experimental results.

- **Optimization Model Construction**: Automatically generates reinforcement learning models for each task, which are combined with evaluation models to create new experimental designs.

- **Continuous Iterative Improvement**: Uses feedback from real experimental data to update models and solutions, ensuring continuous improvement in experimental outcomes.

## Key Features

- **End-to-End Automation**: From solution generation to experimental optimization.

- **Dynamic Adaptation**: Real-time adaptation to unseen tasks using LLMs and expert models.

- **Iterative Feedback Loop**: Incorporates real experimental results to refine models and solutions.

- **Scalable Design**: Supports a wide range of experimental tasks and objectives.

Coming soon...