Simon Blanke
Simon Blanke
I would like to introduce a new feature to Hyperactive to **chain together** multiple optimization algorithms. This will be called an **Optimization Strategy** in the future. The API for this...
## Explanation It would be very useful if Hyperactive has the ability to **save** the optimization backend (via pickle, dill, cloudpickle, ...) to disk and **load** it later into Hyperactive...
An interesting example for gradient-free-optimization is fitting one or multiple gauss functions to data. The data can be generated with numpy for this example. A "real world" example of this...
Since Hyperactive allows a lot of flexibility in creating the objective function, the optimization of hyperparameters, cost function or structure of a siamese network should be possible.
Since Hyperactive is able to perform neural architecture search it would be interesting to have an example of the optimization of a residual neural network. It would then be possible...
In the upcoming v1.1 I will release the Spiral Optimization algorithm. An explanation and visualization can be found on [wikipedia](https://en.wikipedia.org/wiki/Spiral_optimization_algorithm). The current source code of this new algorithm for Gradient-Free-Optimizers...
I looked into a way to add more acquisition functions for the sequence model-based optimization algorithms. In the current version 1.0 the only acquisition function is the expected improvement. Since...
Multiple users requested DIRECT optimizer in this package. This issue will track the process of its implementation and improvement.
In the upcoming v1.1 I will release the Lipschitz Optimizer. . An explanation and visualization can be found on [this blog](http://blog.dlib.net/2017/12/a-global-optimization-algorithm-worth.html). The current source code of this new algorithm for...
The popular python package [ray](https://github.com/ray-project/ray) has a multiprocessing feature that could be used to run optimization-processes in parallel: ```python from ray.util.multiprocessing import Pool def f(index): return index pool = Pool()...