tfp.optimizer.differential_evolution_one_step

Performs one step of the differential evolution algorithm.

tfp.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`.

tfp.optimizer.differential_evolution_one_step

Args

Returns