""" Schaffer Function N.2

Reference: http://en.wikipedia.org/wiki/Test_functions_for_optimization#Test_functions_for_single-objective_optimization_problems """

def __init__(self):

#First we call the constructor of the base class telling

#essentially to PyGMO what kind of problem to expect (1 objective, 0 contraints etc.)

dim = 2 #This is schaffer_function with 2 dimensions

super(schaffer_function_n2,self).__init__(dim)

#then we set the problem bounds (in this case equal for all components)

self.set_bounds(-100,100)

#define instance variable dim

self.__dim = dim

#We reimplement the virtual method that defines the objective function.

def _objfun_impl(self, candidate):

x = candidate[0]

y = candidate[1]

f = 0.5 + (math.sin(x**2 - y**2)**2 - 0.5) / ( 1 + 0.001*(x**2 + y**2) )**2

return (f,)

#Finally we also reimplement a virtual method that adds some output to the __repr__ method

def human_readable_extra(self):

text = """\tProblem dimension: %s

Implemented function:

f = 0.5 + (math.sin(x**2 - y**2)**2 - 0.5) / ( 1 + 0.001*(x**2 + y**2) )**2 """ % str(self.__dim)

""" Sphere Function N dimension

Reference: http://en.wikipedia.org/wiki/Test_functions_for_optimization#Test_functions_for_single-objective_optimization_problems """

def __init__(self, dim = 10):

super(sphere_function,self).__init__(dim)

self.set_bounds(-100,100)

self.__dim = dim

def _objfun_impl(self, x):

f = 0

for i in range(self.__dim):

f += x[i]**2

return (f,)

def human_readable_extra(self):

text = """\tProblem dimension: %s

Implemented function: f = x^2""" % str(self.__dim)