Simon Blanke

Hello @nevinadalal, thanks for helping out! > from our understanding you need a function whose input is a dataset and should return the parameters of the Gaussian curves that fit...

this feature was released in v1.2

Hello @Vanshikas253, I am very interested in your solution! :-) if you open a pull-request we can take a look together.

The idea of this feature is to reduce the size of the search-space after an optimization algorithm inside a strategy has finished its run. Before starting the next algorithm the...

Over time the command-line output of this package has grown. With multiprocessing the output can be confusingly large. I would like to improve this design (maybe with color, indentation or...

Sequence-model-based optimization algorithms in this package work by predicting the best approximate score from a list of all possible positions in the search-space. The current implementation never changes or modifies...

[This should do the trick](https://github.com/SimonBlanke/Gradient-Free-Optimizers/commit/ab16dbcdee8aa753bd98d325d6449d9c899990a5) and some [parameter-testing](https://github.com/SimonBlanke/Gradient-Free-Optimizers/commit/83a0aeb23afeb418ce3a0d34d5423ebf9ef61d76)

Hello @logan-dunbar, sorry for this very late answer. I read you comment and looked into the link you provided, but answering you somehow fell of my radar. Using `low` and...

Hello @mxv001, thanks for your suggestion. I looked into the nevergrad-package. The interface you have shown is somewhat related to this issue, because it also enables continuous parameter ranges. But...

This new parameter would determine if the optimum the algorithm is searching for is the minimum or maximum of the objective-function. The API for this could look as follows: ```python...