Slow/Freeze with powerful configuration

Hi there,

With the cluster of my university, I'm trying to run TPOT for a classification problem on a jupyter notebook with a datset of size (166'158x193). I cannot provide the dataset for confidentialy reasons.

Despite:
- powerful instance (36 cores, 180 GB RAM)
- tuning parameters ("TPOT LIGHT", reducing generation and population_size)
- consulting other issues (reinstall TPOT development branch,...)
- using Dask

The fitting time is vers slow and the progress bar only progress if i stop the kernel:

Here is my configuration:

Here is my dask cluster:

Here is my TPOT code:
```python
pipeline_optimizer = TPOTClassifier(generations=20,
population_size=50,
scoring='neg_log_loss',
cv=ut.RepeatedHoldout(n_iter=30, train_size=0.6, test_size=0.1),
random_state=42,
verbosity=2,
n_jobs=-1,
warm_start = True,
use_dask=True)
pipeline_optimizer.fit(df_cv[xvars].values, df_cv[yvars].values, weight)
```

where `ut.RepeatedHoldout()` is a personal cross-validation generator to deal with Time-dependant data
So I'm wondering if missed any important parameters to run smoothly TPOT,...

Thank you for any help