  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.
