Rename batched.ipynb to batched_searches.ipynb and update it to inclu…

…de more detailed descriptions

examples/notebooks/Functional_API/README.md

@@ -4,6 +4,6 @@ As an alternative to its object-oriented stateful API, EvoTorch provides an API
 
 Here are the examples demonstrating various features of this functional API:
 
-- **[Maintaining a batch of populations using the functional EvoTorch API](batched.ipynb)**: This notebook shows how one can run optimization on a batch of populations, and how one can assign a different hyperparameter configuration for each batch item.
+- **[Maintaining a batch of populations using the functional EvoTorch API](batched_searches.ipynb)**: This notebook shows how one can efficiently run multiple searches simultaneously, each with its own population and hyperparameter configuration, by maintaining a batch of populations.
 - **[Solving constrained optimization problems](constrained.ipynb)**: EvoTorch provides batching-friendly constraint penalization functions that can be used with both the object-oriented API and the functional API. In addition, these constraint penalization functions can be used with gradient-based optimization. This notebook demonstrates these features.
 - **[Solving reinforcement learning tasks using functional evolutionary algorithms](problem.ipynb)**: The functional evolutionary algorithm implementations of EvoTorch can be used to solve problems that are expressed using the object-oriented core API of EvoTorch. To demonstrate this, this notebook instantiates a `GymNE` problem for the reinforcement learning task "CartPole-v1", and solves it using the functional `pgpe` implementation.