# {py:mod}`evox.algorithms.so.de_variants.jade`

```{py:module} evox.algorithms.so.de_variants.jade
```

```{autodoc2-docstring} evox.algorithms.so.de_variants.jade
:allowtitles:
```

## Module Contents

### Classes

````{list-table}
:class: autosummary longtable
:align: left

* - {py:obj}`JaDE <evox.algorithms.so.de_variants.jade.JaDE>`
  - ```{autodoc2-docstring} evox.algorithms.so.de_variants.jade.JaDE
    :summary:
    ```
````

### API

`````{py:class} JaDE(pop_size: int, lb: torch.Tensor, ub: torch.Tensor, num_difference_vectors: int = 1, mean: torch.Tensor | None = None, stdev: torch.Tensor | None = None, c: float = 0.1, device: torch.device | None = None)
:canonical: evox.algorithms.so.de_variants.jade.JaDE

Bases: {py:obj}`evox.core.Algorithm`

```{autodoc2-docstring} evox.algorithms.so.de_variants.jade.JaDE
```

```{rubric} Initialization
```

```{autodoc2-docstring} evox.algorithms.so.de_variants.jade.JaDE.__init__
```

````{py:method} init_step()
:canonical: evox.algorithms.so.de_variants.jade.JaDE.init_step

```{autodoc2-docstring} evox.algorithms.so.de_variants.jade.JaDE.init_step
```

````

````{py:method} step()
:canonical: evox.algorithms.so.de_variants.jade.JaDE.step

```{autodoc2-docstring} evox.algorithms.so.de_variants.jade.JaDE.step
```

````

````{py:method} _select_rand_pbest_vectors(p: float) -> torch.Tensor
:canonical: evox.algorithms.so.de_variants.jade.JaDE._select_rand_pbest_vectors

```{autodoc2-docstring} evox.algorithms.so.de_variants.jade.JaDE._select_rand_pbest_vectors
```

````

`````