The OpenELM Library: Leveraging Progress in Language Models for Novel Evolutionary Algorithms

Herbie Bradley, Honglu Fan, Theodoros Galanos, Ryan Zhou, Daniel Scott, Joel Lehman

Abstract:

In recent years, Large Language Models (LLMs) have rapidly
progressed in their capabilities in natural language processing (NLP) tasks,
which have interestingly grown in scope to include generating computer
programs. Indeed, recent studies have demonstrated how LLMs can
enable highly proficient genetic programming (GP) algorithms and novel
evolutionary algorithms more broadly. Motivated by these opportunities,
this paper introduces OpenELM , an open-source Python library for
designing evolutionary algorithms that leverage LLMs to intelligently
generate variation, as well as to assess fitness and measures of diversity.
The library includes implementations of several variation operators, and
is designed to accommodate those with limited compute resources, by
enabling fast inference, being runnable through hosted notebooks (such
as Google Colab), and allowing for API-based LLMs to be used instead
of local models run on GPUs. Additionally, OpenELM includes a variety
of domain implementations for easy experimentation and adaptation,
including several GP domains. The hope is to help researchers easily
develop new approaches and applications within the nascent and largely
unexplored paradigm of evolutionary algorithms that leverage LLMs.