# {py:mod}`evox.problems.numerical.cec2022`

```{py:module} evox.problems.numerical.cec2022
```

```{autodoc2-docstring} evox.problems.numerical.cec2022
:allowtitles:
```

## Module Contents

### Classes

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

* - {py:obj}`CEC2022 <evox.problems.numerical.cec2022.CEC2022>`
  - ```{autodoc2-docstring} evox.problems.numerical.cec2022.CEC2022
    :summary:
    ```
````

### API

`````{py:class} CEC2022(problem_number: int, dimension: int, device: torch.device | None = None)
:canonical: evox.problems.numerical.cec2022.CEC2022

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

```{autodoc2-docstring} evox.problems.numerical.cec2022.CEC2022
```

```{rubric} Initialization
```

```{autodoc2-docstring} evox.problems.numerical.cec2022.CEC2022.__init__
```

````{py:method} shift(x: torch.Tensor, offset: torch.Tensor) -> torch.Tensor
:canonical: evox.problems.numerical.cec2022.CEC2022.shift

```{autodoc2-docstring} evox.problems.numerical.cec2022.CEC2022.shift
```

````

````{py:method} rotate(x: torch.Tensor, M: torch.Tensor) -> torch.Tensor
:canonical: evox.problems.numerical.cec2022.CEC2022.rotate

```{autodoc2-docstring} evox.problems.numerical.cec2022.CEC2022.rotate
```

````

````{py:method} cut(x: torch.Tensor, Gp: typing.List[float], sh_flag: bool, rot_flag: bool, offset: torch.Tensor, M: torch.Tensor) -> typing.List[torch.Tensor]
:canonical: evox.problems.numerical.cec2022.CEC2022.cut

```{autodoc2-docstring} evox.problems.numerical.cec2022.CEC2022.cut
```

````

````{py:method} sr_func_rate(x: torch.Tensor, sh_rate: float, sh_flag: bool, rot_flag: bool, offset: torch.Tensor, M: torch.Tensor) -> torch.Tensor
:canonical: evox.problems.numerical.cec2022.CEC2022.sr_func_rate

```{autodoc2-docstring} evox.problems.numerical.cec2022.CEC2022.sr_func_rate
```

````

````{py:method} cf_cal(x: torch.Tensor, fit: typing.List[torch.Tensor], delta: typing.List[int], bias: typing.List[int]) -> torch.Tensor
:canonical: evox.problems.numerical.cec2022.CEC2022.cf_cal

```{autodoc2-docstring} evox.problems.numerical.cec2022.CEC2022.cf_cal
```

````

````{py:method} cec2022_f1(x: torch.Tensor) -> torch.Tensor
:canonical: evox.problems.numerical.cec2022.CEC2022.cec2022_f1

```{autodoc2-docstring} evox.problems.numerical.cec2022.CEC2022.cec2022_f1
```

````

````{py:method} cec2022_f2(x: torch.Tensor) -> torch.Tensor
:canonical: evox.problems.numerical.cec2022.CEC2022.cec2022_f2

```{autodoc2-docstring} evox.problems.numerical.cec2022.CEC2022.cec2022_f2
```

````

````{py:method} cec2022_f3(x: torch.Tensor) -> torch.Tensor
:canonical: evox.problems.numerical.cec2022.CEC2022.cec2022_f3

```{autodoc2-docstring} evox.problems.numerical.cec2022.CEC2022.cec2022_f3
```

````

````{py:method} cec2022_f4(x: torch.Tensor) -> torch.Tensor
:canonical: evox.problems.numerical.cec2022.CEC2022.cec2022_f4

```{autodoc2-docstring} evox.problems.numerical.cec2022.CEC2022.cec2022_f4
```

````

````{py:method} cec2022_f5(x: torch.Tensor) -> torch.Tensor
:canonical: evox.problems.numerical.cec2022.CEC2022.cec2022_f5

```{autodoc2-docstring} evox.problems.numerical.cec2022.CEC2022.cec2022_f5
```

````

````{py:method} cec2022_f6(x: torch.Tensor) -> torch.Tensor
:canonical: evox.problems.numerical.cec2022.CEC2022.cec2022_f6

```{autodoc2-docstring} evox.problems.numerical.cec2022.CEC2022.cec2022_f6
```

````

````{py:method} cec2022_f7(x: torch.Tensor) -> torch.Tensor
:canonical: evox.problems.numerical.cec2022.CEC2022.cec2022_f7

```{autodoc2-docstring} evox.problems.numerical.cec2022.CEC2022.cec2022_f7
```

````

````{py:method} cec2022_f8(x: torch.Tensor) -> torch.Tensor
:canonical: evox.problems.numerical.cec2022.CEC2022.cec2022_f8

```{autodoc2-docstring} evox.problems.numerical.cec2022.CEC2022.cec2022_f8
```

````

````{py:method} cec2022_f9(x: torch.Tensor) -> torch.Tensor
:canonical: evox.problems.numerical.cec2022.CEC2022.cec2022_f9

```{autodoc2-docstring} evox.problems.numerical.cec2022.CEC2022.cec2022_f9
```

````

````{py:method} cec2022_f10(x: torch.Tensor) -> torch.Tensor
:canonical: evox.problems.numerical.cec2022.CEC2022.cec2022_f10

```{autodoc2-docstring} evox.problems.numerical.cec2022.CEC2022.cec2022_f10
```

````

````{py:method} cec2022_f11(x: torch.Tensor) -> torch.Tensor
:canonical: evox.problems.numerical.cec2022.CEC2022.cec2022_f11

```{autodoc2-docstring} evox.problems.numerical.cec2022.CEC2022.cec2022_f11
```

````

````{py:method} cec2022_f12(x: torch.Tensor) -> torch.Tensor
:canonical: evox.problems.numerical.cec2022.CEC2022.cec2022_f12

```{autodoc2-docstring} evox.problems.numerical.cec2022.CEC2022.cec2022_f12
```

````

````{py:method} levy_func(x: torch.Tensor) -> torch.Tensor
:canonical: evox.problems.numerical.cec2022.CEC2022.levy_func

```{autodoc2-docstring} evox.problems.numerical.cec2022.CEC2022.levy_func
```

````

````{py:method} bent_cigar_func(x: torch.Tensor) -> torch.Tensor
:canonical: evox.problems.numerical.cec2022.CEC2022.bent_cigar_func

```{autodoc2-docstring} evox.problems.numerical.cec2022.CEC2022.bent_cigar_func
```

````

````{py:method} hgbat_func(x: torch.Tensor) -> torch.Tensor
:canonical: evox.problems.numerical.cec2022.CEC2022.hgbat_func

```{autodoc2-docstring} evox.problems.numerical.cec2022.CEC2022.hgbat_func
```

````

````{py:method} katsuura_func(x: torch.Tensor) -> torch.Tensor
:canonical: evox.problems.numerical.cec2022.CEC2022.katsuura_func

```{autodoc2-docstring} evox.problems.numerical.cec2022.CEC2022.katsuura_func
```

````

````{py:method} modified_schwefel_func(x: torch.Tensor) -> torch.Tensor
:canonical: evox.problems.numerical.cec2022.CEC2022.modified_schwefel_func

```{autodoc2-docstring} evox.problems.numerical.cec2022.CEC2022.modified_schwefel_func
```

````

````{py:method} schaffer_F7_func(x: torch.Tensor) -> torch.Tensor
:canonical: evox.problems.numerical.cec2022.CEC2022.schaffer_F7_func

```{autodoc2-docstring} evox.problems.numerical.cec2022.CEC2022.schaffer_F7_func
```

````

````{py:method} escaffer6_func(x: torch.Tensor) -> torch.Tensor
:canonical: evox.problems.numerical.cec2022.CEC2022.escaffer6_func

```{autodoc2-docstring} evox.problems.numerical.cec2022.CEC2022.escaffer6_func
```

````

````{py:method} happycat_func(x: torch.Tensor) -> torch.Tensor
:canonical: evox.problems.numerical.cec2022.CEC2022.happycat_func

```{autodoc2-docstring} evox.problems.numerical.cec2022.CEC2022.happycat_func
```

````

````{py:method} grie_rosen_func(x: torch.Tensor) -> torch.Tensor
:canonical: evox.problems.numerical.cec2022.CEC2022.grie_rosen_func

```{autodoc2-docstring} evox.problems.numerical.cec2022.CEC2022.grie_rosen_func
```

````

````{py:method} discus_func(x: torch.Tensor) -> torch.Tensor
:canonical: evox.problems.numerical.cec2022.CEC2022.discus_func

```{autodoc2-docstring} evox.problems.numerical.cec2022.CEC2022.discus_func
```

````

````{py:method} ellips_func(x: torch.Tensor) -> torch.Tensor
:canonical: evox.problems.numerical.cec2022.CEC2022.ellips_func

```{autodoc2-docstring} evox.problems.numerical.cec2022.CEC2022.ellips_func
```

````

````{py:method} evaluate(pop: torch.Tensor) -> torch.Tensor
:canonical: evox.problems.numerical.cec2022.CEC2022.evaluate

````

`````