diff --git a/docs/yolov5/tutorials/hyperparameter_evolution.md b/docs/yolov5/tutorials/hyperparameter_evolution.md
index 0ed2c94c56..6ca148a906 100644
--- a/docs/yolov5/tutorials/hyperparameter_evolution.md
+++ b/docs/yolov5/tutorials/hyperparameter_evolution.md
@@ -100,7 +100,6 @@ done
 ```
 
 The default evolution settings will run the base scenario 300 times, i.e. for 300 generations. You can modify generations via the `--evolve` argument, i.e. `python train.py --evolve 1000`.
-https://github.com/ultralytics/yolov5/blob/6a3ee7cf03efb17fbffde0e68b1a854e80fe3213/train.py#L608
 
 The main genetic operators are **crossover** and **mutation**. In this work mutation is used, with an 80% probability and a 0.04 variance to create new offspring based on a combination of the best parents from all previous generations. Results are logged to `runs/evolve/exp/evolve.csv`, and the highest fitness offspring is saved every generation as `runs/evolve/hyp_evolved.yaml`:
 
diff --git a/ultralytics/__init__.py b/ultralytics/__init__.py
index c1c5dd1396..f73ef84b80 100644
--- a/ultralytics/__init__.py
+++ b/ultralytics/__init__.py
@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = '8.0.167'
+__version__ = '8.0.168'
 
 from ultralytics.models import RTDETR, SAM, YOLO
 from ultralytics.models.fastsam import FastSAM
diff --git a/ultralytics/engine/tuner.py b/ultralytics/engine/tuner.py
index 443d01d1e3..0702690df5 100644
--- a/ultralytics/engine/tuner.py
+++ b/ultralytics/engine/tuner.py
@@ -159,16 +159,18 @@ class Tuner:
         """
 
         t0 = time.time()
+        best_save_dir, best_metrics = None, None
         self.tune_dir.mkdir(parents=True, exist_ok=True)
         for i in range(iterations):
             # Mutate hyperparameters
             mutated_hyp = self._mutate()
             LOGGER.info(f'{prefix} Starting iteration {i + 1}/{iterations} with hyperparameters: {mutated_hyp}')
 
-            # Initialize and train YOLOv8 model
             try:
+                # Train YOLO model with mutated hyperparameters
                 train_args = {**vars(self.args), **mutated_hyp}
-                fitness = (deepcopy(model) or YOLO(self.args.model)).train(**train_args).fitness  # results.fitness
+                results = (deepcopy(model) or YOLO(self.args.model)).train(**train_args)
+                fitness = results.fitness
             except Exception as e:
                 LOGGER.warning(f'WARNING ❌️ training failure for hyperparameter tuning iteration {i}\n{e}')
                 fitness = 0.0
@@ -179,14 +181,25 @@ class Tuner:
             with open(self.evolve_csv, 'a') as f:
                 f.write(headers + ','.join(map(str, log_row)) + '\n')
 
-        # Print tuning results
-        x = np.loadtxt(self.evolve_csv, ndmin=2, delimiter=',', skiprows=1)
-        fitness = x[:, 0]  # first column
-        i = np.argsort(-fitness)[0]  # best fitness index
-        LOGGER.info(f'\n{prefix} All iterations complete ✅ ({time.time() - t0:.2f}s)\n'
-                    f'{prefix} Results saved to {colorstr("bold", self.tune_dir)}\n'
-                    f'{prefix} Best fitness={fitness[i]} observed at iteration {i}')
-
-        # Save turning results
-        yaml_save(self.tune_dir / 'best.yaml', data={k: float(x[0, i + 1]) for i, k in enumerate(self.space.keys())})
-        yaml_print(self.tune_dir / 'best.yaml')
+            # Print tuning results
+            x = np.loadtxt(self.evolve_csv, ndmin=2, delimiter=',', skiprows=1)
+            fitness = x[:, 0]  # first column
+            best_idx = fitness.argmax()
+            best_is_current = best_idx == i
+            if best_is_current:
+                best_save_dir = results.save_dir
+                best_metrics = {k: round(v, 5) for k, v in results.results_dict.items()}
+            header = (f'{prefix} {i + 1} iterations complete ✅ ({time.time() - t0:.2f}s)\n'
+                      f'{prefix} Results saved to {colorstr("bold", self.tune_dir)}\n'
+                      f'{prefix} Best fitness={fitness[best_idx]} observed at iteration {best_idx + 1}\n'
+                      f'{prefix} Best fitness metrics are {best_metrics}\n'
+                      f'{prefix} Best fitness model is {best_save_dir}\n'
+                      f'{prefix} Best fitness hyperparameters are printed below.\n')
+
+            LOGGER.info('\n' + header)
+
+            # Save turning results
+            data = {k: float(x[0, i + 1]) for i, k in enumerate(self.space.keys())}
+            header = header.replace(prefix, '#').replace('/', '').replace('', '') + '\n'
+            yaml_save(self.tune_dir / 'best.yaml', data=data, header=header)
+            yaml_print(self.tune_dir / 'best.yaml')
diff --git a/ultralytics/utils/__init__.py b/ultralytics/utils/__init__.py
index 0fc67a2e27..4c06a27a2f 100644
--- a/ultralytics/utils/__init__.py
+++ b/ultralytics/utils/__init__.py
@@ -286,13 +286,14 @@ class ThreadingLocked:
         return decorated
 
 
-def yaml_save(file='data.yaml', data=None):
+def yaml_save(file='data.yaml', data=None, header=''):
     """
     Save YAML data to a file.
 
     Args:
         file (str, optional): File name. Default is 'data.yaml'.
         data (dict): Data to save in YAML format.
+        header (str, optional): YAML header to add.
 
     Returns:
         (None): Data is saved to the specified file.
@@ -311,6 +312,8 @@ def yaml_save(file='data.yaml', data=None):
 
     # Dump data to file in YAML format
     with open(file, 'w') as f:
+        if header:
+            f.write(header)
         yaml.safe_dump(data, f, sort_keys=False, allow_unicode=True)
 
 
diff --git a/ultralytics/utils/autobatch.py b/ultralytics/utils/autobatch.py
index 252f3db81f..4e9ed07c7c 100644
--- a/ultralytics/utils/autobatch.py
+++ b/ultralytics/utils/autobatch.py
@@ -29,7 +29,7 @@ def check_train_batch_size(model, imgsz=640, amp=True):
         return autobatch(deepcopy(model).train(), imgsz)  # compute optimal batch size
 
 
-def autobatch(model, imgsz=640, fraction=0.67, batch_size=DEFAULT_CFG.batch):
+def autobatch(model, imgsz=640, fraction=0.60, batch_size=DEFAULT_CFG.batch):
     """
     Automatically estimate the best YOLO batch size to use a fraction of the available CUDA memory.