diff --git a/epde/interface/equation_translator.py b/epde/interface/equation_translator.py
index 572f9d4c..e81723cb 100755
--- a/epde/interface/equation_translator.py
+++ b/epde/interface/equation_translator.py
@@ -216,7 +216,7 @@ def __init__(self, lp_terms : Union[list, tuple, dict], rp_term : Union[list, tu
             metaparameters={'terms_number': {'optimizable': False, 'value': len(term_list)},
                             'max_factors_in_term': {'optimizable': False, 'value': max_factors}}
             for var_key in all_vars:
-                metaparameters[('sparsity', var_key)] = {'optimizable': True, 'value': 1.}
+                metaparameters[('sparsity', var_key)] = {'optimizable': True, 'value': 0.}
 
 
             self.equation = Equation(pool=pool, basic_structure=terms_aggregated,
diff --git a/epde/operators/common/fitness.py b/epde/operators/common/fitness.py
index 145a3851..957b982e 100644
--- a/epde/operators/common/fitness.py
+++ b/epde/operators/common/fitness.py
@@ -200,7 +200,7 @@ def apply(self, objective: Equation, arguments: dict, force_out_of_place: bool =
                 for start_idx in range(0, num_horizons, step_size):
                     end_idx = start_idx + window_size
                     target_window = target_vals[start_idx:end_idx]
-                    eq_window_weights.append(np.abs(np.std(target_window) / np.mean(target_window)))
+                    eq_window_weights.append(np.abs(np.std(target_window) / np.sqrt(np.mean(np.power(target_window, 2)))))
                 lr = np.mean(eq_window_weights)
             else:
                 features = self.feature_reshape(features_vals)
@@ -212,7 +212,7 @@ def apply(self, objective: Equation, arguments: dict, force_out_of_place: bool =
                     estimator.fit(feature_window, target_window, sample_weight=self.g_fun_vals[start_idx:end_idx])
                     valuable_weights = estimator.coef_[:-1]
                     eq_window_weights.append(valuable_weights)
-                eq_cv = np.array([np.abs(np.std(_) / (np.mean(_) + 1e-12)) for _ in zip(*eq_window_weights)])
+                eq_cv = np.array([np.abs(np.std(_) / np.sqrt(np.mean(np.power(_, 2)))) for _ in zip(*eq_window_weights)])
                 lr = eq_cv.mean()
 
         elif target_vals.ndim == 2:
@@ -233,7 +233,7 @@ def apply(self, objective: Equation, arguments: dict, force_out_of_place: bool =
                             target_window = target_vals[start_idx:end_idx, :].reshape(-1)
                         else:
                             target_window = target_vals[:, start_idx:end_idx].reshape(-1)
-                        eq_window_weights.append(np.abs(np.std(target_window) / np.mean(target_window)))
+                        eq_window_weights.append(np.abs(np.std(target_window) / np.sqrt(np.mean(np.power(target_window, 2)))))
                     lr += np.mean(eq_window_weights)
                 else:
                     features = self.feature_reshape(features_vals)
@@ -250,7 +250,7 @@ def apply(self, objective: Equation, arguments: dict, force_out_of_place: bool =
                             estimator.fit(feature_window, target_window, sample_weight=self.g_fun_vals.reshape(*data_shape, -1)[:, start_idx:end_idx].reshape(-1))
                         valuable_weights = estimator.coef_[:-1]
                         eq_window_weights.append(valuable_weights)
-                    eq_cv = np.array([np.abs(np.std(_) / (np.mean(_) + 1e-12)) for _ in zip(*eq_window_weights)])
+                    eq_cv = np.array([np.abs(np.std(_) / np.sqrt(np.mean(np.power(_, 2)))) for _ in zip(*eq_window_weights)])
                     lr += eq_cv.mean()
 
         elif target_vals.ndim == 3:
@@ -269,7 +269,7 @@ def apply(self, objective: Equation, arguments: dict, force_out_of_place: bool =
                             target_window = target_vals[:, start_idx:end_idx, :].reshape(-1)
                         else:
                             target_window = target_vals[:, :, start_idx:end_idx].reshape(-1)
-                        eq_window_weights.append(np.abs(np.std(target_window) / np.mean(target_window)))
+                        eq_window_weights.append(np.abs(np.std(target_window) / np.sqrt(np.mean(np.power(target_window, 2)))))
                     lr += np.mean(eq_window_weights)
                 else:
                     features = self.feature_reshape(features_vals)
@@ -290,7 +290,7 @@ def apply(self, objective: Equation, arguments: dict, force_out_of_place: bool =
                             estimator.fit(feature_window, target_window, sample_weight=self.g_fun_vals.reshape(*data_shape, -1)[:, :, start_idx:end_idx].reshape(-1))
                         valuable_weights = estimator.coef_[:-1]
                         eq_window_weights.append(valuable_weights)
-                    eq_cv = np.array([np.abs(np.std(_) / (np.mean(_) + 1e-12)) for _ in zip(*eq_window_weights)])
+                    eq_cv = np.array([np.abs(np.std(_) / np.sqrt(np.mean(np.power(_, 2)))) for _ in zip(*eq_window_weights)])
                     lr += eq_cv.mean()
 
         objective.fitness_calculated = True