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I am exploring some ideas for evaluating a configuration space across different instances and was wondering if there was a native way to support weighting the instances. The reason for this is that th…
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New environments to be created based on:
* [Direct Shape Optimization through Deep Reinforcement Learning](https://arxiv.org/pdf/1908.09885.pdf)
* [Aerodynamic Shape Optimization using a Novel Opt…
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It is unclear to me how to interpret objective senses vs weights in the multi-objective mode.
While solvers intepret the sign of the weight as the sense (actually even more than that, see below) --…
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So, I played around with pygad a bit and realized that you only get the best solution of the previous generation. This is particularly noticeable with a stop criteria.
I would like to put the stres…
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If not, is the purpose of the additionalMetricNames parameter just for visualization? It will not affect the experimental results?
Please help me answer this question, thank you very much !!!🙏
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Hey @sgbaird!
This repo is super cool! It is great to see Ax is useful for these optimization problems.
In the interest of lightweight R&D, it would be awesome if this repo had multi-fidelity su…
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Risk should be another dimension for the multi-objective optimization.
See [weisberg_poster_v1.pptx](https://github.com/ProjectTorreyPines/FUSE.jl/files/9767668/weisberg_poster_v1.pptx)
Risk informa…
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The current multiobjective benchmarks are based on `ArtificialFunction` which is initially mono-objective. We should use more "native" multiobjective benchmarks, such as the ones presented on the wiki…
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I am having a small example to do multi-objective optimization for given set of hyper parameters.
Here is the code:
```
algorithm= [0, 1, 2, 3]
archs=[0, 1, 2, 3, 4]
kernel=[0, 1, 2, 3, 4, 5]
n…
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To make signal-processing based methods as strong as possible, for comparison with model-based methods (e.g., neural network) we would ideally be able to "fit" the signal-processing methods.
My bes…