# `Jido.Evolve.Examples.HyperparameterTuning`
[🔗](https://github.com/agentjido/jido_evolve/blob/v1.0.0/lib/examples/hyperparameter_tuning.ex#L1)

Hyperparameter Optimization: Evolve ML model hyperparameters for best performance.

## Problem Description

Optimize hyperparameters (learning rate, layer sizes, dropout, activation)
to maximize model validation accuracy. Uses a surrogate fitness function
that simulates training without running actual neural networks.

This demonstrates:
- **Mixed-type parameters**: Floats, integers, lists, and enums
- **Schema-driven evolution**: Type-aware mutations
- **Log-scale optimization**: Learning rate in log space
- **Caching**: Avoid re-evaluating identical configurations

## Genome Representation

Hyperparameter map:

    %{
      learning_rate: 0.001,
      hidden_layers: [128, 64],
      dropout_rate: 0.2,
      activation: :relu,
      batch_size: 32
    }

## Fitness Evaluation

Surrogate function rewards:
- Moderate learning rates (0.0001-0.01)
- 2-3 hidden layers
- Medium layer sizes (64-128)
- Low dropout
- Specific activations (:relu, :gelu)

## Usage

    iex> Jido.Evolve.Examples.HyperparameterTuning.run()
    # Evolution progress shown...
    # Converges to good hyperparameter configuration

## Expected Results

Converges to validation accuracy >0.85 in 50-100 generations,
demonstrating schema-aware mutation and crossover.

# `batch_evaluate`

Default implementation of batch_evaluate/2 that delegates to shared implementation.

Raises on invalid fitness results. Override to customize error handling.

# `demo`

# `run`

---

*Consult [api-reference.md](api-reference.md) for complete listing*