Cannot reproduce pipeline results with sklearn pipeline

For my data, I got the best pipeline by running TPOT training using the following parameters:

```python
from tpot import TPOTClassifier
tpot = TPOTClassifier(generations=5,
population_size=100,
verbosity=2,
n_jobs=-1,random_state=1)

```
The best pipeline was given as:

```python
Best pipeline: MLPClassifier(GaussianNB(Binarizer(input_matrix, threshold=0.0)), alpha=0.001, learning_rate_init=0.001)
TPOTClassifier(generations=5, n_jobs=-1, random_state=1, verbosity=2)
```
The best CV score I achieved was 0.822

Using the ensemble provided above I trained an ensemble pipeline using sklearn as:

```python
base_model = GaussianNB()

meta_model = MLPClassifier(random_state=1,
learning_rate_init=0.001,
alpha=0.001)

ensemble = StackingClassifier(estimators=[('base_model', base_model),
('meta_model', meta_model)],
final_estimator=meta_model,
n_jobs=-1)
```

The score I get from this is 0.79

Can you tell me why I getting different scores when all my parameters are same?