<div dir="ltr">This sounds like an algorithm for parallel protein folding that I ranted about a long time ago. Start with some collection of conformations; perform many different molecular dynamics simulations from your starting points in parallel; continue with the most promising subset. As molecular dynamics on proteins tends to find lots of deadends, you can get a lot of improvement by tabu'ing the known deadends and extending into conformations which don't double back into visited regions. Seems I remember it went back to some monte-carlo work at LANL in the 1950's, Goodfellow? <div><br></div><div>It also sounds a lot like Monte Carlo Tree search as used, for instance, in AlphaGo.<div><br></div><div>It boils down to how well you can distinguish promising and unpromising branches.<br><div><br></div><div>Whatever, it was in Friam before gmail, so I can't search for it. There doesn't appear to be any search in the Friam archives, and the years before 2017 at <a href="https://redfish.com/pipermail/friam_redfish.com/">https://redfish.com/pipermail/friam_redfish.com/</a> are all 404 anyway.</div></div><div><br></div><div>-- rec --</div></div></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Mon, Aug 30, 2021 at 12:39 PM uǝlƃ ☤>$ <<a href="mailto:gepropella@gmail.com">gepropella@gmail.com</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex">In my ignorance, I've thought of weighted ensemble (WE) as a specific kind of novelty search. E.g. weighting toward trajectories that exhibit anomalies. Is that what you mean by it?<br>
<br>
Also, for each of the 5 you're interested in, do you have convenient example cites for each/any of them? In particular, (2) and (3)? Or are these just ideas of places where you think WE should apply?<br>
<br>
For my part, no. I haven't used WE in particular. I have a friend who's worked on identifying mechanical anomalies from audio (recordings of machines as they hum). He may have used it. I'll ask. <br>
<br>
On 8/29/21 1:07 PM, Jon Zingale wrote:<br>
> I am presently working on learning weighted ensemble <<a href="https://arxiv.org/pdf/1906.00856.pdf" rel="noreferrer" target="_blank">https://arxiv.org/pdf/1906.00856.pdf</a>> sampling techniques and was curious if any here have worked with them before. The technique seems promising and has enjoyed quite a bit of success (even above MCMC <<a href="https://en.wikipedia.org/wiki/Markov_chain_Monte_Carlo" rel="noreferrer" target="_blank">https://en.wikipedia.org/wiki/Markov_chain_Monte_Carlo</a>>) in circles concerned with reaction rates for rare events.<br>
> <br>
> Some points of interest for me include:<br>
> <br>
> 1. A better sampling of fringe-outlier works/art from streaming services.<br>
> 2. An alternative (bin-based sampling) to globally defined "fitness" measures in evolutionary modeling.<br>
> 3. An application of diffusion-limited aggregation to general search (especially in the face of limited resources)<br>
> 4. An application of linear logic to optimization problems in conformation prediction <<a href="https://en.wikipedia.org/wiki/Protein_structure_prediction" rel="noreferrer" target="_blank">https://en.wikipedia.org/wiki/Protein_structure_prediction</a>>.<br>
> 5. Investigation of dynamical properties, such as distribution of trajectories with "high winding number", on strange attractors.<br>
> <br>
> <br>
> While I am just beginning to grok the technique, I thought it might be fruitful to ask here.<br>
> <br>
> Jon<br>
<br>
<br>
-- <br>
☤>$ uǝlƃ<br>
<br>
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