class_docstr: Xor objective function.
  
  This class defines the Xor [1]_ global optimization problem. This is a
  multimodal minimization problem defined as follows:
  
  .. math::
  
      f_{\text{Xor}}(x) = \left[ 1 + \exp \left( - \frac{x_7}{1 +
      \exp(-x_1 - x_2 - x_5)} - \frac{x_8}{1 + \exp(-x_3 - x_4 - x_6)}
      - x_9 \right ) \right ]^{-2} \\
      + \left [ 1 + \exp \left( -\frac{x_7}{1 + \exp(-x_5)}
      - \frac{x_8}{1 + \exp(-x_6)} - x_9 \right ) \right] ^{-2} \\
      + \left [1 - \left\{1 + \exp \left(-\frac{x_7}{1 + \exp(-x_1 - x_5)}
      - \frac{x_8}{1 + \exp(-x_3 - x_6)} - x_9 \right ) \right\}^{-1}
      \right ]^2 \\
      + \left [1 - \left\{1 + \exp \left(-\frac{x_7}{1 + \exp(-x_2 - x_5)}
      - \frac{x_8}{1 + \exp(-x_4 - x_6)} - x_9 \right ) \right\}^{-1}
      \right ]^2
  
  
  with :math:`x_i \in [-1, 1]` for :math:`i=1,...,9`.
  
  *Global optimum*: :math:`f(x) = 0.9597588` for
  :math:`\x = [1, -1, 1, -1, -1, 1, 1, -1, 0.421134]`
  
  .. [1] Gavana, A. Global Optimization Benchmarks and AMPGO retrieved 2015
code: def __init__(self, dimensions=9):
      Benchmark.__init__(self, dimensions)
  
      self._bounds = list(zip([-1.0] * self.N, [1.0] * self.N))
  
      self.global_optimum = [
          [1.0, -1.0, 1.0, -1.0, -1.0, 1.0, 1.0, -1.0, 0.421134]
      ]
      self.fglob = 0.9597588
  
imports: import numpy
  from numpy import abs, sum, sin, cos, pi, exp, arange, prod, sqrt
  from .go_benchmark import Benchmark
label_desc: Use image node_id 1 to create a new Xor object from inherited base classes: Benchmark with example: obj = Xor(dimensions)