If biology is the study of self-replicating entities, and we want to
understand the role of information, it makes sense to see how
information theory is connected to the 'replicator equation' — a
simple model of population dynamics for self-replicating entities.
The relevant concept of information turns out to be the information of
one probability distribution relative to another, also known as the
Kullback–Leibler divergence. Using this we can get a new
outlook on free energy, see evolution as a learning process, and give
a clearer, more general formulation of Fisher's fundamental theorem of natural
selection.

You can see the the slides for this talk, and also this
video:

In the video there's a typo which I fixed later in the slides:
\( \exp(-kE_i/T) \) should be \(\exp(-kE_i/T) \). For more, read: