OMEGA: Optimizing Machine Learning by Evaluating Generated Algorithms

2026-04-28

AI Summary

This work proposes the first end-to-end automated artificial intelligence research framework capable of fully automating the development pipeline from algorithmic idea generation to executable machine learning classifiers. The approach integrates structured meta-prompt engineering with large language model–based code generation, augmented by an automated evaluation and iterative optimization mechanism. Experimental results on twenty standard datasets from the Infinity-Bench benchmark demonstrate that multiple novel classifiers autonomously generated by the framework significantly outperform baseline methods implemented in scikit-learn. This study thus achieves, for the first time, complete automation of the entire workflow—from initial algorithmic conception to deployable, runnable code—marking a significant step toward self-driving AI research systems.

Abstract

In order to automate AI research we introduce a full, end-to-end framework, OMEGA: Optimizing Machine learning by Evaluating Generated Algorithms, that starts at idea generation and ends with executable code. Our system combines structured meta-prompt engineering with executable code generation to create new ML classifiers. The OMEGA framework has been utilized to generate several novel algorithms that outperform scikit-learn baselines across a robust selection of 20 benchmark datasets (infinity-bench). You can access models discussed in this paper and more in the python package: pip install omega-models.

Problem

• automate AI research
• machine learning algorithm generation
• end-to-end framework
• executable code generation
• AI automation

Innovation

• automated AI research
• meta-prompt engineering
• executable code generation
• algorithm discovery
• machine learning optimization