diff --git a/bayes_opt/parameter.py b/bayes_opt/parameter.py
index 1bc2e0b1..a8955e5b 100644
--- a/bayes_opt/parameter.py
+++ b/bayes_opt/parameter.py
@@ -489,7 +489,10 @@ def __call__(self, X: Any, Y: Any = None, eval_gradient: bool = False) -> Any:
         def __reduce__(self) -> str | tuple[Any, ...]:
             return (wrap_kernel, (kernel, transform))
 
-    return WrappedKernel(**kernel.get_params())
+    wrapped_instance = WrappedKernel.__new__(WrappedKernel)
+    wrapped_instance.__dict__.update(kernel.__dict__)
+    wrapped_instance._transform = transform
+    return wrapped_instance
 
 
 def _copy_signature(source_fct: Callable[..., Any]) -> Callable[[Callable[..., Any]], Callable[..., Any]]:
diff --git a/tests/test_parameter.py b/tests/test_parameter.py
index b2394a45..752656de 100644
--- a/tests/test_parameter.py
+++ b/tests/test_parameter.py
@@ -244,3 +244,17 @@ def test_wrapped_kernel_fit():
     gp.fit(space.params, space.target)
 
     assert gp.kernel_.length_scale != 1e5
+
+
+def test_combined_wrapped_kernel_fit():
+    pbounds = {"p1": (0, 1), "p2": (1, 10, int)}
+    space = TargetSpace(None, pbounds)
+
+    space.register(space.random_sample(0), 1.0)
+    space.register(space.random_sample(1), 5.0)
+
+    kernel_fct = kernels.Matern(nu=2.5, length_scale=1e5) + kernels.WhiteKernel(noise_level=1.0)
+    kernel = wrap_kernel(kernel_fct, space.kernel_transform)
+    gp = GaussianProcessRegressor(kernel=kernel, alpha=1e-6, n_restarts_optimizer=5)
+
+    gp.fit(space.params, space.target)