diff --git a/examples/optimizer/meta_optimizer.py b/examples/optimizer/meta_optimizer.py
index cfc7688b..72e71721 100644
--- a/examples/optimizer/meta_optimizer.py
+++ b/examples/optimizer/meta_optimizer.py
@@ -7,7 +7,8 @@
                     inner_cv=KFold(n_splits=5),
                     outer_cv=KFold(n_splits=3),
                     optimizer='switch',
-                    optimizer_params={'name': 'sk_opt', 'n_configurations': 50},
+                    optimizer_params={'name': 'grid_search'},
+                    # optimizer_params={'name': 'random_search', 'n_configurations': 50},
                     metrics=['accuracy', 'precision', 'recall', 'balanced_accuracy'],
                     best_config_metric='accuracy',
                     project_folder='./tmp',
@@ -16,7 +17,7 @@
 my_pipe.add(PipelineElement('StandardScaler'))
 
 my_pipe += PipelineElement('PCA',
-                           hyperparameters={'n_components': IntegerRange(10, 30)},
+                           hyperparameters={'n_components': IntegerRange(10, 30, step=5)},
                            test_disabled=True)
 
 # set up two learning algorithms in an ensemble
@@ -25,10 +26,10 @@
 estimator_selection += PipelineElement('RandomForestClassifier',
                                        criterion='gini',
                                        hyperparameters={'min_samples_split': IntegerRange(2, 4),
-                                                        'max_features': ['auto', 'sqrt', 'log2'],
+                                                        'max_features': ['sqrt', 'log2'],
                                                         'bootstrap': [True, False]})
 estimator_selection += PipelineElement('SVC',
-                                       hyperparameters={'C': FloatRange(0.5, 25),
+                                       hyperparameters={'C': FloatRange(0.5, 25, num=10),
                                                         'kernel': ['linear', 'rbf']})
 
 my_pipe += estimator_selection
@@ -36,4 +37,4 @@
 X, y = load_breast_cancer(return_X_y=True)
 my_pipe.fit(X, y)
 
-my_pipe.results_handler.get_mean_of_best_validation_configs_per_estimator()
+print(my_pipe.results_handler.get_mean_of_best_validation_configs_per_estimator())