OMEGA: Optimizing Machine learning by Evaluating Generated Algorithms

Annika Kaul Singh, Jeremy Nixon

Keywords: Self Improving, Machine Learning, Generated Algorithms, Classification, Automated

TL;DR: Presenting a pipeline to automate discovery, evaluation, and improvement of novel classification algorithms.

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.

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Submission Number: 10