axiter : int, optional
    The maximum number of generations over which the entire population is
    evolved. The maximum number of function evaluations (with no polishing)
    is: ``(maxiter + 1) * popsize * (N - N_equal)``
popsize : int, optional
    A multiplier for setting the total population size. The population has
    ``popsize * (N - N_equal)`` individuals. This keyword is overridden if
    an initial population is supplied via the `init` keyword. When using
    ``init='sobol'`` the population size is calculated as the next power
    of 2 after ``popsize * (N - N_equal)``.
tol : float, optional
    Relative tolerance for convergence, the solving stops when
    ``np.std(population_energies) <= atol + tol * np.abs(np.mean(population_energies))``,
    where and `atol` and `tol` are the absolute and relative tolerance
    respectively.
mutation : float or tuple(float, float), optional
    The mutation constant. In the literature this is also known as
    differential weight, being denoted by F.
    If specified as a float it should be in the range [0, 2].
    If specified as a tuple ``(min, max)`` dithering is employed. Dithering
    randomly changes the mutation constant on a generation by generation
    basis. The mutation constant for that generation is taken from
    U[min, max). Dithering can help speed convergence significantly.
    Increasing the mutation constant increases the search radius, but will
    slow down convergence.
recombination : float, optional
    The recombination constant, should be in the range [0, 1]. In the
    literature this is also known as the crossover probability, being
    denoted by CR. Increasing this value allows a larger number of mutants
    to progress into the next generation, but at the risk of population
    stability.

rng : {None, int, `numpy.random.Generator`}, optional
    
    ..versionchanged:: 1.15.0
        As part of the `SPEC-007 <https://scientific-python.org/specs/spec-0007/>`_
        transition from use of `numpy.random.RandomState` to
        `numpy.random.Generator` this keyword was changed from `seed` to `rng`.
        For an interim period both keywords will continue to work (only specify
        one of them). After the interim period using the `seed` keyword will emit
        warnings. The behavior of the `seed` and `rng` keywords is outlined below.

    If `rng` is passed by keyword, types other than `numpy.random.Generator` are
    passed to `numpy.random.default_rng` to instantiate a `Generator`.
    If `rng` is already a `Generator` instance, then the provided instance is
    used.
    
    If this argument is passed by position or `seed` is passed by keyword, the
    behavior is:
    
    - If `seed` is None (or `np.random`), the `numpy.random.RandomState`
      singleton is used.
    - If `seed` is an int, a new `RandomState` instance is used,
      seeded with `seed`.
    - If `seed` is already a `Generator` or `RandomState` instance then
      that instance is used.
    
    Specify `seed`/`rng` for repeatable minimizations.
disp : bool, optional
    Prints the evaluated `func` at every iteration.
callback : callable, optional
    A callable called after each iteration. Has the signature:

        ``callback(intermediate_result: OptimizeResult)``

    where ``intermediate_result`` is a keyword parameter containing an
    `OptimizeResult` with attributes ``x`` and ``fun``, the best solution
    found so far and the objective function. Note that the name
    of the parameter must be ``intermediate_result`` for the callback
    to be passed an `OptimizeResult`.

    The callback also supports a signature like:

        ``callback(x, convergence: float=val)``

    ``val`` represents the fractional value of the population convergence.
     When ``val`` is greater than ``1.0``, the function halts.

    Introspection is used to determine which of the signatures is invoked.

    Global minimization will halt if the callback raises ``StopIteration``
    or returns ``True``; any polishing is still carried out.

    .. versionchanged:: 1.12.0
        callback accepts the ``intermediate_result`` keyword.

polish : bool, optional
    If True (default), then `scipy.optimize.minimize` with the `L-BFGS-B`
    method is used to polish the best population member at the end, which
    can improve the minimization slightly. If a constrained problem is
    being studied then the `trust-constr` method is used instead. For large
    problems with many constraints, polishing can take a long time due to
    the Jacobian computations.
maxfun : int, optional
    Set the maximum number of function evaluations. However, it probably
    makes more sense to set `maxiter` instead.
init : str or array-like, optional
    Specify which type of population initialization is performed. Should be
    one of:

        - 'latinhypercube'
        - 'sobol'
        - 'halton'
        - 'random'
        - array specifying the initial population. The array should have
          shape ``(S, N)``, where S is the total population size and
          N is the number of parameters.
          `init` is clipped to `bounds` before use.

    The default is 'latinhypercube'. Latin Hypercube sampling tries to
    maximize coverage of the available parameter space.

    'sobol' and 'halton' are superior alternatives and maximize even more
    the parameter space. 'sobol' will enforce an initial population
    size which is calculated as the next power of 2 after
    ``popsize * (N - N_equal)``. 'halton' has no requirements but is a bit
    less efficient. See `scipy.stats.qmc` for more details.

    'random' initializes the population randomly - this has the drawback
    that clustering can occur, preventing the whole of parameter space
    being covered. Use of an array to specify a population could be used,
    for example, to create a tight bunch of initial guesses in an location
    where the solution is known to exist, thereby reducing time for
    convergence.
atol : float, optional
    Absolute tolerance for convergence, the solving stops when
    ``np.std(pop) <= atol + tol * np.abs(np.mean(population_energies))``,
    where and `atol` and `tol` are the absolute and relative tolerance
    respectively.
updating : {'immediate', 'deferred'}, optional
    If ``'immediate'``, the best solution vector is continuously updated
    within a single generation [4]_. This can lead to faster convergence as
    trial vectors can take advantage of continuous improvements in the best
    solution.
    With ``'deferred'``, the best solution vector is updated once per
    generation. Only ``'deferred'`` is compatible with parallelization or
    vectorization, and the `workers` and `vectorized` keywords can
    over-ride this option.
workers : int or map-like callable, optional
    If `workers` is an int the population is subdivided into `workers`
    sections and evaluated in parallel
    (uses `multiprocessing.Pool <multiprocessing>`).
    Supply `-1` to use all cores available to the Process.
    Alternatively supply a map-like callable, such as
    `multiprocessing.Pool.map` for evaluating the population in parallel.
    This evaluation is carried out as ``workers(func, iterable)``.
    This option will override the `updating` keyword to
    `updating='deferred'` if `workers != 1`.
    Requires that `func` be pickleable.
constraints : {NonLinearConstraint, LinearConstraint, Bounds}
    Constraints on the solver, over and above those applied by the `bounds`
    kwd. Uses the approach by Lampinen.
x0 : None or array-like, optional
    Provides an initial guess to the minimization. Once the population has
    been initialized this vector replaces the first (best) member. This
    replacement is done even if `init` is given an initial population.
    ``x0.shape == (N,)``.
integrality : 1-D array, optional
    For each decision variable, a boolean value indicating whether the
    decision variable is constrained to integer values. The array is
    broadcast to ``(N,)``.
    If any decision variables are constrained to be integral, they will not
    be changed during polishing.
    Only integer values lying between the lower and upper bounds are used.
    If there are no integer values lying between the bounds then a
    `ValueError` is raised.
vectorized : bool, optional
    If ``vectorized is True``, `func` is sent an `x` array with
    ``x.shape == (N, S)``, and is expected to return an array of shape
    ``(S,)``, where `S` is the number of solution vectors to be calculated.
    If constraints are applied, each of the functions used to construct
    a `Constraint` object should accept an `x` array with
    ``x.shape == (N, S)``, and return an array of shape ``(M, S)``, where
    `M` is the number of constraint components.
    This option is an alternative to the parallelization offered by
    `workers`, and may help in optimization speed. This keyword is
    ignored if ``workers != 1``.
    This option will override the `updating` keyword to
    ``updating='deferred'``.
