TPOTRegressor returns score of -inf when data increases

Trying to use TPOTRegressor on my own dataset:

```
from tpot import TPOTRegressor
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
import pandas as pd
from sklearn.metrics import mean_squared_error
hhsz_ = pd.read_pickle('data_hhsz.pkl')
X_train, X_test, y_train, y_test = train_test_split(hhsz_.head(100000)[[i for i in hhsz_.columns if 'id' not in i and 'biweek' not in i and i !='hh_size']].astype('float').values,hhsz_.head(100000)['hh_size'].astype('float').values,train_size=0.75, test_size=0.25, random_state=42)
tpot = TPOTRegressor(generations=1, population_size=1, verbosity=3, random_state=42)
tpot.fit(X_train, y_train)
print(tpot.score(X_test, y_test))
tpot.export('tpot_hhsz_pipeline.py')
```

## Context of the issue

However, the code works when I use the first 10000 rows of my data but will fail with more data used. The error message doesn't provide any clue why this is happening. I also double checked the data and they are all float with no missing values. Any suggestion where I can start to figure out which part is failing? tpopt==0.11.7 and sklearn==0.24.1

I'm expecting the output to be something like

```
Generation 1 - Current best internal CV score: -3.3265091734288665

Best pipeline: RandomForestRegressor(input_matrix, bootstrap=True, max_features=0.7500000000000001, min_samples_leaf=16, min_samples_split=9, n_estimators=100)
-3.1596568188745064
```

## Current result

```
Generation 1 - Current best internal CV score: -inf
Traceback (most recent call last):
File "/home/hadoop/zhan/zhlib/anaconda3/envs/python37/lib/python3.7/site-packages/tpot/base.py", line 828, in fit
log_file=self.log_file_,
File "/home/hadoop/zhan/zhlib/anaconda3/envs/python37/lib/python3.7/site-packages/tpot/gp_deap.py", line 281, in eaMuPlusLambda
per_generation_function(gen)
File "/home/hadoop/zhan/zhlib/anaconda3/envs/python37/lib/python3.7/site-packages/tpot/base.py", line 1176, in _check_periodic_pipeline
self._update_top_pipeline()
File "/home/hadoop/zhan/zhlib/anaconda3/envs/python37/lib/python3.7/site-packages/tpot/base.py", line 935, in _update_top_pipeline
"There was an error in the TPOT optimization "
RuntimeError: There was an error in the TPOT optimization process. This could be because the data was not formatted properly, or because data for a regression problem was provided to the TPOTClassifier object. Please make sure you passed the data to TPOT correctly. If you enabled PyTorch estimators, please check the data requirements in the online documentation: https://epistasislab.github.io/tpot/using/

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "run.py", line 8, in
tpot.fit(X_train, y_train)
File "/home/hadoop/zhan/zhlib/anaconda3/envs/python37/lib/python3.7/site-packages/tpot/base.py", line 863, in fit
raise e
File "/home/hadoop/zhan/zhlib/anaconda3/envs/python37/lib/python3.7/site-packages/tpot/base.py", line 854, in fit
self._update_top_pipeline()
File "/home/hadoop/zhan/zhlib/anaconda3/envs/python37/lib/python3.7/site-packages/tpot/base.py", line 935, in _update_top_pipeline
"There was an error in the TPOT optimization "
RuntimeError: There was an error in the TPOT optimization process. This could be because the data was not formatted properly, or because data for a regression problem was provided to the TPOTClassifier object. Please make sure you passed the data to TPOT correctly. If you enabled PyTorch estimators, please check the data requirements in the online documentation: https://epistasislab.github.io/tpot/using/
```