*Last updated: 2023-03-16.*
# tf_quant_finance.math.optimizer.differential_evolution_one_step
Performs one step of the differential evolution algorithm.
```python
tf_quant_finance.math.optimizer.differential_evolution_one_step(
objective_function, population, population_values=None, differential_weight=0.5,
crossover_prob=0.9, seed=None, name=None
)
```
#### Args:
* `objective_function`: A Python callable that accepts a batch of possible
solutions and returns the values of the objective function at those
arguments as a rank 1 real `Tensor`. This specifies the function to be
minimized. The input to this callable may be either a single `Tensor`
or a Python `list` of `Tensor`s. The signature must match the format of
the argument `population`. (i.e., objective_function(*population) must
return the value of the function to be minimized).
* `population`: `Tensor` or Python `list` of `Tensor`s representing the
current population vectors. Each `Tensor` must be of the same real dtype.
The first dimension indexes individual population members while the
rest of the dimensions are consumed by the value function. For example,
if the population is a single `Tensor` of shape [n, m1, m2], then `n` is
the population size and the output of `objective_function` applied to the
population is a `Tensor` of shape [n]. If the population is a python
list of `Tensor`s then each `Tensor` in the list should have the first
axis of a common size, say `n` and `objective_function(*population)`
should return a `Tensor` of shape [n]. The population must have at least
4 members for the algorithm to work correctly.
* `population_values`: A `Tensor` of rank 1 and real dtype. The result of
applying `objective_function` to the `population`. If not supplied it is
computed using the `objective_function`.
Default value: None.
* `differential_weight`: Real scalar `Tensor`. Must be positive and less than
2.0. The parameter controlling the strength of mutation.
Default value: 0.5
* `crossover_prob`: Real scalar `Tensor`. Must be between 0 and 1. The
probability of recombination per site.
Default value: 0.9
* `seed`: `int` or None. The random seed for this `Op`. If `None`, no seed is
applied.
Default value: None.
* `name`: (Optional) Python str. The name prefixed to the ops created by this
function. If not supplied, the default name 'one_step' is
used.
Default value: None
#### Returns:
A sequence containing the following elements (in order):
* `next_population`: A `Tensor` or Python `list` of `Tensor`s of the same
structure as the input population. The population at the next generation.
* `next_population_values`: A `Tensor` of same shape and dtype as input
`population_values`. The function values for the `next_population`.