parameters: (self, gen=100, cr=0.95, eta_c=10, m=0.01, eta_m=10)
docstring: Constructs a Non-dominated Sorting Genetic Algorithm (NSGA_II)
  
      USAGE: algorithm.nsga_II(self, gen=100, cr = 0.95, eta_c = 10, m = 0.01, eta_m = 10)
  
      * gen: number of generations
      * cr: crossover factor [0,1[
      * eta_c: Distribution index for crossover
      * m: mutation probability [0,1]
      * eta_m: Distribution index for mutation
docstring_summary: Constructs a Non-dominated Sorting Genetic Algorithm (NSGA_II)
docstring_tokens: Constructs a Non - dominated Sorting Genetic Algorithm ( NSGA_II )
function: def _nsga_II_ctor(self, gen=100, cr=0.95, eta_c=10, m=0.01, eta_m=10):
      """
      Constructs a Non-dominated Sorting Genetic Algorithm (NSGA_II)
  
      USAGE: algorithm.nsga_II(self, gen=100, cr = 0.95, eta_c = 10, m = 0.01, eta_m = 10)
  
      * gen: number of generations
      * cr: crossover factor [0,1[
      * eta_c: Distribution index for crossover
      * m: mutation probability [0,1]
      * eta_m: Distribution index for mutation
      """
      # We set the defaults or the kwargs
      arg_list = []
      arg_list.append(gen)
      arg_list.append(cr)
      arg_list.append(eta_c)
      arg_list.append(m)
      arg_list.append(eta_m)
      self._orig_init(*arg_list)
function_tokens: def _nsga_II_ctor ( self , gen = 100 , cr = 0.95 , eta_c = 10 , m = 0.01 , eta_m = 10 ) : # We set the defaults or the kwargs arg_list = [ ] arg_list . append ( gen ) arg_list . append ( cr ) arg_list . append ( eta_c ) arg_list . append ( m ) arg_list . append ( eta_m ) self . _orig_init ( * arg_list )