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# scipy.optimize.differential_evolution

`scipy.optimize.``differential_evolution`(func, bounds, args=(), strategy='best1bin', maxiter=1000, popsize=15, tol=0.01, mutation=(0.5, 1), recombination=0.7, seed=None, callback=None, disp=False, polish=True, init='latinhypercube', atol=0)

  Finds the global minimum of a multivariate function. Differential Evolution is stochastic in nature (does not use gradient methods) to find the minimium, and can search large areas of candidate space, but often requires larger numbers of function evaluations than conventional gradient based techniques.

  The algorithm is due to Storn and Price [R164].

  Parameters:

  func : callable

  > The objective function to be minimized. Must be in the form `f(x, *args)`, where `x` is the argument in the form of a 1-D array and `args` is a tuple of any additional fixed parameters needed to completely specify the function.

  bounds : sequence

  > Bounds for variables. `(min, max)` pairs for each element in `x`, defining the lower and upper bounds for the optimizing argument of func. It is required to have `len(bounds) == len(x)`. `len(bounds)` is used to determine the number of parameters in `x`.

  args : tuple, optional

  > Any additional fixed parameters needed to completely specify the objective function.

  strategy : str, optional

  > The differential evolution strategy to use. Should be one of:

  > > - ‘best1bin’
  > > - ‘best1exp’
  > > - ‘rand1exp’
  > > - ‘randtobest1exp’
  > > - ‘best2exp’
  > > - ‘rand2exp’
  > > - ‘randtobest1bin’
  > > - ‘best2bin’
  > > - ‘rand2bin’
  > > - ‘rand1bin’

  > The default is ‘best1bin’.

  maxiter : 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 * len(x)`

  popsize : int, optional

  > A multiplier for setting the total population size. The population has `popsize * len(x)` individuals.

  tol : float, optional

  > Relative 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.

  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.

  seed : int or np.random.RandomState, optional

  > If seed is not specified the np.RandomState singleton is used. If seed is an int, a new np.random.RandomState instance is used, seeded with seed. If seed is already a np.random.RandomState instance, then that np.random.RandomState instance is used. Specify seed for repeatable minimizations.

  disp : bool, optional

  > Display status messages

  callback : callable, callback(xk, convergence=val), optional

  > A function to follow the progress of the minimization. `xk` is the current value of `x0`. `val` represents the fractional value of the population convergence. When `val` is greater than one the function halts. If callback returns True, then the minimization is halted (any polishing is still carried out).

  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.

  init : string, optional

  > Specify how the population initialization is performed. Should be one of:

  > > - ‘latinhypercube’
  > > - ‘random’

  > The default is ‘latinhypercube’. Latin Hypercube sampling tries to maximize coverage of the available parameter space. ‘random’ initializes the population randomly - this has the drawback that clustering can occur, preventing the whole of parameter space being covered.

  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.

  Returns:

  res : OptimizeResult

  > The optimization result represented as a `OptimizeResult` object. Important attributes are: `x` the solution array, `success` a Boolean flag indicating if the optimizer exited successfully and `message` which describes the cause of the termination. See `OptimizeResult` for a description of other attributes. If polish was employed, and a lower minimum was obtained by the polishing, then OptimizeResult also contains the `jac` attribute.

  Notes

  Differential evolution is a stochastic population based method that is useful for global optimization problems. At each pass through the population the algorithm mutates each candidate solution by mixing with other candidate solutions to create a trial candidate. There are several strategies [R165] for creating trial candidates, which suit some problems more than others. The ‘best1bin’ strategy is a good starting point for many systems. In this strategy two members of the population are randomly chosen. Their difference is used to mutate the best member (the best in best1bin), \(b_0\), so far:

  \[b' = b_0 + mutation * (population[rand0] - population[rand1])\]

  A trial vector is then constructed. Starting with a randomly chosen ‘i’th parameter the trial is sequentially filled (in modulo) with parameters from b’ or the original candidate. The choice of whether to use b’ or the original candidate is made with a binomial distribution (the ‘bin’ in ‘best1bin’) - a random number in [0, 1) is generated. If this number is less than the recombination constant then the parameter is loaded from b’, otherwise it is loaded from the original candidate. The final parameter is always loaded from b’. Once the trial candidate is built its fitness is assessed. If the trial is better than the original candidate then it takes its place. If it is also better than the best overall candidate it also replaces that. To improve your chances of finding a global minimum use higher popsize values, with higher mutation and (dithering), but lower recombination values. This has the effect of widening the search radius, but slowing convergence.

  New in version 0.15.0.

  References

  [R164] | (1, 2) Storn, R and Price, K, Differential Evolution - a Simple and Efficient Heuristic for Global Optimization over Continuous Spaces, Journal of Global Optimization, 1997, 11, 341 - 359.

  [R165] | (1, 2) http://www1.icsi.berkeley.edu/~storn/code.html

  [R166] | http://en.wikipedia.org/wiki/Differential_evolution

  Examples

  Let us consider the problem of minimizing the Rosenbrock function. This function is implemented in `rosen` in `scipy.optimize`.

      >>> from scipy.optimize import rosen, differential_evolution
      >>> bounds = [(0,2), (0, 2), (0, 2), (0, 2), (0, 2)]
      >>> result = differential_evolution(rosen, bounds)
      >>> result.x, result.fun
      (array([1., 1., 1., 1., 1.]), 1.9216496320061384e-19)

  Next find the minimum of the Ackley function (http://en.wikipedia.org/wiki/Test_functions_for_optimization).

      >>> from scipy.optimize import differential_evolution
      >>> import numpy as np
      >>> def ackley(x):
      ...     arg1 = -0.2 * np.sqrt(0.5 * (x[0] ** 2 + x[1] ** 2))
      ...     arg2 = 0.5 * (np.cos(2. * np.pi * x[0]) + np.cos(2. * np.pi * x[1]))
      ...     return -20. * np.exp(arg1) - np.exp(arg2) + 20. + np.e
      >>> bounds = [(-5, 5), (-5, 5)]
      >>> result = differential_evolution(ackley, bounds)
      >>> result.x, result.fun
      (array([ 0.,  0.]), 4.4408920985006262e-16)