code: def parse_individual(self, individual):
          
          scaled_ind = []
          for i in range(len(self.value_means)):
              scaled_ind.append(self.value_means[i] + (
                  individual[i] * self.value_ranges[i]))
          fullpars = list(self.arrangement)
          for k in range(len(self.variable_parameters)):
              for j in range(len(fullpars)):
                  if fullpars[j] == self.variable_parameters[k]:
                      fullpars[j] = scaled_ind[k]
          return fullpars
docs: Converts a deap individual into a full list of parameters.
  
          Parameters
          ----------
          individual: deap individual from optimization
              Details vary according to type of optimization, but
              parameters within deap individual are always between -1
              and 1. This function converts them into the values used to
              actually build the model
  
          Returns
          -------
          fullpars: list
              Full parameter list for model building.