🚀 go-pugleaf

When talking about either genetic algorithms or neural networks to people,
I usually say that these fields were inspired by the physical models, but 
are certainly not a serious attempt to model either genetics or nervous 
systems.

I believe that we algorithmicists need to be quite careful in how we 
use the vocabulary of other fields, especially if the algorithm is 
using that field as a neat analogy rather than as a model.  Scientists
spend much painful work deriving a useful vocabulary for their area of
research, and have every right to resent careless redefinitions of their
terms from people that don't understand the standard definitions of those 
terms at all well.  Science is fighting a terrible tower-of-babel effect
as it is without needless redefinition, often for what seems to be just
the ability to have snappy words for the titles of papers.

That said, I believe that both fields would benefit tremendously from
closer interaction with their biological namesakes. We should encourage
this interaction and listen carefully to the complexity of their world.
In the sort run, we are more likely to gain from that interaction than
they are.

David Rogers