Interacting with Gurobi for Function evaluation

[j-alex-vindel]

Hi, I have been working to use NSGAII to find the Pareto frontier from a mmo problem, I am using pymoo to do the NSGAII part of the solution to generate a population and the genes to evaluate, I then use gurobi to solve the model and get the function values.

import numpy as np
from pymoo.core.problem import ElementwiseProblem
import gurobipy as gp
from gurobipy import GRB
from pymoo.termination import get_termination
from pymoo.optimize import minimize
from pymoo.algorithms.moo.nsga2 import NSGA2
from pymoo.operators.crossover.pntx import TwoPointCrossover
from pymoo.operators.mutation.bitflip import BitflipMutation

My_Problem(ElementwiseProblem):
   def __init__(self,n_arcs:int=None,network=None):
        super().__init__(n_var=n_arcs,n_obj=2,n_constr=0,xl=0,xu=1,type_var=bool)
        self.n_arcs = n_arcs
        self.network = network
   def solve_with_gurobi(self,x_bin,K):
        # add gurobi's  continuous variables v
        # add multiobjective v1,v2
        # 1k linear constraints
        # Knapsack contraint involing sum(x_bin) == K
        # lb*x_bin <= v <= ub*x_bin
        return v1,v2
  def _evaluate(self,x_bin,out):
        K = 2
        x_bin_rounded = np.round(x_bin).astype(int)
        solv = self.solve_gurobi(K,x_bin_rounded)
        if solv is None:
            out["F"] = [1e10,1e10]
        else:
            f1,f2 = solv 
            out["F"] = [f1,f2]
 

mn = # a network defined with the number of arcs and all other data needed 
problem = My_Problem(n_arcs=len(mn.M),network=mn)

algorithm = NSGA2(pop_size = 100,
                    crossover = TwoPointCrossover(),
                    mutation = BitflipMutation(),
                  eliminate_duplicates = True)

termination = get_termination("n_eval",1000)

res = minimize(problem,algorithm,
               termination,
               seed=1,
               verbose=True)

However, when solving I get the following error:
Xp[flip] = ~X[flip]
TypeError: ufunc 'invert' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''

I am at a loss, I was suggested to use pymoo's binary sampling but I can't find the correct documentation for it. Could anyone help?