Update YOLO11 Actions and Docs (#16596)

Signed-off-by: UltralyticsAssistant <web@ultralytics.com>

docs/en/guides/hyperparameter-tuning.md

@@ -23,7 +23,7 @@ Hyperparameters are high-level, structural settings for the algorithm. They are
   <img width="640" src="https://github.com/ultralytics/docs/releases/download/0/hyperparameter-tuning-visual.avif" alt="Hyperparameter Tuning Visual">
 </p>
 
-For a full list of augmentation hyperparameters used in YOLOv8 please refer to the [configurations page](../usage/cfg.md#augmentation-settings).
+For a full list of augmentation hyperparameters used in YOLO11 please refer to the [configurations page](../usage/cfg.md#augmentation-settings).
 
 ### Genetic Evolution and Mutation
 
@@ -67,7 +67,7 @@ The process is repeated until either the set number of iterations is reached or
 
 ## Usage Example
 
-Here's how to use the `model.tune()` method to utilize the `Tuner` class for hyperparameter tuning of YOLOv8n on COCO8 for 30 epochs with an AdamW optimizer and skipping plotting, checkpointing and validation other than on final epoch for faster Tuning.
+Here's how to use the `model.tune()` method to utilize the `Tuner` class for hyperparameter tuning of YOLO11n on COCO8 for 30 epochs with an AdamW optimizer and skipping plotting, checkpointing and validation other than on final epoch for faster Tuning.
 
 !!! example
 
@@ -77,7 +77,7 @@ Here's how to use the `model.tune()` method to utilize the `Tuner` class for hyp
         from ultralytics import YOLO
 
         # Initialize the YOLO model
-        model = YOLO("yolov8n.pt")
+        model = YOLO("yolo11n.pt")
 
         # Tune hyperparameters on COCO8 for 30 epochs
         model.tune(data="coco8.yaml", epochs=30, iterations=300, optimizer="AdamW", plots=False, save=False, val=False)
@@ -202,7 +202,7 @@ The hyperparameter tuning process in Ultralytics YOLO is simplified yet powerful
 
 1. [Hyperparameter Optimization in Wikipedia](https://en.wikipedia.org/wiki/Hyperparameter_optimization)
 2. [YOLOv5 Hyperparameter Evolution Guide](../yolov5/tutorials/hyperparameter_evolution.md)
-3. [Efficient Hyperparameter Tuning with Ray Tune and YOLOv8](../integrations/ray-tune.md)
+3. [Efficient Hyperparameter Tuning with Ray Tune and YOLO11](../integrations/ray-tune.md)
 
 For deeper insights, you can explore the `Tuner` class source code and accompanying documentation. Should you have any questions, feature requests, or need further assistance, feel free to reach out to us on [GitHub](https://github.com/ultralytics/ultralytics/issues/new/choose) or [Discord](https://discord.com/invite/ultralytics).
 
@@ -220,7 +220,7 @@ To optimize the learning rate for Ultralytics YOLO, start by setting an initial
         from ultralytics import YOLO
 
         # Initialize the YOLO model
-        model = YOLO("yolov8n.pt")
+        model = YOLO("yolo11n.pt")
 
         # Tune hyperparameters on COCO8 for 30 epochs
         model.tune(data="coco8.yaml", epochs=30, iterations=300, optimizer="AdamW", plots=False, save=False, val=False)
@@ -228,9 +228,9 @@ To optimize the learning rate for Ultralytics YOLO, start by setting an initial
 
 For more details, check the [Ultralytics YOLO configuration page](../usage/cfg.md#augmentation-settings).
 
-### What are the benefits of using genetic algorithms for hyperparameter tuning in YOLOv8?
+### What are the benefits of using genetic algorithms for hyperparameter tuning in YOLO11?
 
-Genetic algorithms in Ultralytics YOLOv8 provide a robust method for exploring the hyperparameter space, leading to highly optimized model performance. Key benefits include:
+Genetic algorithms in Ultralytics YOLO11 provide a robust method for exploring the hyperparameter space, leading to highly optimized model performance. Key benefits include:
 
 - **Efficient Search**: Genetic algorithms like mutation can quickly explore a large set of hyperparameters.
 - **Avoiding Local Minima**: By introducing randomness, they help in avoiding local minima, ensuring better global optimization.
@@ -240,7 +240,7 @@ To see how genetic algorithms can optimize hyperparameters, check out the [hyper
 
 ### How long does the hyperparameter tuning process take for Ultralytics YOLO?
 
-The time required for hyperparameter tuning with Ultralytics YOLO largely depends on several factors such as the size of the dataset, the complexity of the model architecture, the number of iterations, and the computational resources available. For instance, tuning YOLOv8n on a dataset like COCO8 for 30 epochs might take several hours to days, depending on the hardware.
+The time required for hyperparameter tuning with Ultralytics YOLO largely depends on several factors such as the size of the dataset, the complexity of the model architecture, the number of iterations, and the computational resources available. For instance, tuning YOLO11n on a dataset like COCO8 for 30 epochs might take several hours to days, depending on the hardware.
 
 To effectively manage tuning time, define a clear tuning budget beforehand ([internal section link](#preparing-for-hyperparameter-tuning)). This helps in balancing resource allocation and optimization goals.