class_docstr: Goldstein-Price objective function.
  
  This class defines the Goldstein-Price [1]_ global optimization problem. This
  is a multimodal minimization problem defined as follows:
  
  .. math::
  
      f_{\text{GoldsteinPrice}}(x) = \left[ 1 + (x_1 + x_2 + 1)^2 
      (19 - 14 x_1 + 3 x_1^2 - 14 x_2 + 6 x_1 x_2 + 3 x_2^2) \right]
      \left[ 30 + ( 2x_1 - 3 x_2)^2 (18 - 32 x_1 + 12 x_1^2
      + 48 x_2 - 36 x_1 x_2 + 27 x_2^2) \right]
  
  
  with :math:`x_i \in [-2, 2]` for :math:`i = 1, 2`.
  
  *Global optimum*: :math:`f(x) = 3` for :math:`x = [0, -1]`
  
  .. [1] Jamil, M. & Yang, X.-S. A Literature Survey of Benchmark Functions
  For Global Optimization Problems Int. Journal of Mathematical Modelling
  and Numerical Optimisation, 2013, 4, 150-194.
code: def __init__(self, dimensions=2):
      Benchmark.__init__(self, dimensions)
  
      self._bounds = list(zip([-2.0] * self.N, [2.0] * self.N))
  
      self.global_optimum = [[0.0, -1.0]]
      self.fglob = 3.0
  
imports: import numpy
  from numpy import abs, sin, cos, exp, floor, log, arange, prod, sqrt, sum
  from .go_benchmark import Benchmark
label_desc: Use image node_id 1 to create a new GoldsteinPrice object from inherited base classes: Benchmark with example: obj = GoldsteinPrice(dimensions)