Closed-Form Test Functions for Biophysical Sequence Optimization Algorithms

TL;DR

This paper introduces Ehrlich functions, a new class of closed-form test functions designed for biophysical sequence optimization, providing a valuable benchmark to facilitate research and development in this domain.

Contribution

The paper proposes Ehrlich functions as novel, simplified benchmarks that capture key geometric features of biophysical problems, aiding algorithm development.

Findings

Ehrlich functions are non-trivial to optimize with standard genetic algorithms.

They serve as effective benchmarks for biophysical sequence optimization.

Empirical results validate the usefulness of Ehrlich functions in research.

Abstract

There is a growing body of work seeking to replicate the success of machine learning (ML) on domains like computer vision (CV) and natural language processing (NLP) to applications involving biophysical data. One of the key ingredients of prior successes in CV and NLP was the broad acceptance of difficult benchmarks that distilled key subproblems into approachable tasks that any junior researcher could investigate, but good benchmarks for biophysical domains are rare. This scarcity is partially due to a narrow focus on benchmarks which simulate biophysical data; we propose instead to carefully abstract biophysical problems into simpler ones with key geometric similarities. In particular we propose a new class of closed-form test functions for biophysical sequence optimization, which we call Ehrlich functions. We provide empirical results demonstrating these functions are interesting objects of study and can be non-trivial to solve with a standard genetic optimization baseline.