how to use pymoo to update the coefficient vectors of a machine learning model about pymoo HOT 1 CLOSED

import numpy as np
from sklearn import linear_model

from pymoo.algorithms.soo.nonconvex.de import DE
from pymoo.core.problem import ElementwiseProblem
from pymoo.operators.sampling.lhs import LHS
from pymoo.optimize import minimize

A = np.array([[0, 0], [0, 0], [1, 1]])
b = np.array([0, .1, 1])
alpha = 0.5

reg = linear_model.Ridge(alpha=alpha)
reg.fit(A, b)
print(reg.intercept_, reg.coef_)


class RidgeRegression(ElementwiseProblem):

    def __init__(self):
        super().__init__(n_var=3, n_obj=1, xl=-10, xu=+10)

    def _evaluate(self, x, out, *args, **kwargs):
        coef_ = x[:2]
        intercept_ = x[2]
        y_hat = A @ coef_ + intercept_

        out['coef'] = coef_
        out['intercept'] = intercept_
        out['F'] = ((b - y_hat) ** 2).sum() + alpha * (coef_ ** 2).sum()


problem = RidgeRegression()

algorithm = DE(
    pop_size=100,
    sampling=LHS(),
    variant="DE/rand/1/bin",
    CR=0.3,
    dither="vector",
    jitter=False
)

res = minimize(problem,
               algorithm,
               seed=1,
               verbose=False)

sol = res.opt[0]
print(sol.get('intercept'), sol.get('coef'))