Thanks for your intersest and questions. I've quoted all that seems relevant from your posts below and put my answers in between.
1) It will be easier for people (at least for me) to think about possible approaches if the overall goal of the project is more clear. What are we trying to do? What would a successful approach lead to? (See my earlier post.)
The objective is to improve convergence rate of the optimization process. We hypothesize that a human-aided approach would help with this. If an improved algorithm achieves the same goal (we tried various alternatives), this would also be OK.
2) Can we get a complete data set? (The comment I had wanted to put up was about the available data set being just 2074 records, when it says there are tens of thousands available. It seems like the whole thing could easily be released.)
The reasons for the small number of records in the example dataset: The data comes from an attempt to parameterize a new model, started just recently, and it is just one model variant of many that we are are about to fit. We can provide the current full dataset if this would be useful at this point.
* Are the iterations numbers accurately representing ordered runs by the evolutionary algorithm? In many cases (like parameter_5) the choices seem to be actually getting more wide-spread over time, rather than narrowing in on an ideal value. Or is this just a hint that this...
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