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

Knapsack Problem: Select items to maximize value without exceeding weight capacity.

## Problem Description

You have a knapsack with limited weight capacity and a collection of items,
each with a weight and value. The goal is to select items that maximize total
value without exceeding the weight limit.

This demonstrates why genetic algorithms excel:
- **Building blocks**: Good item combinations can be inherited
- **Crossover value**: Mixing two good solutions often produces better offspring
- **Selection pressure**: Invalid solutions (too heavy) are penalized

## Genome Representation

Binary vector where 1 = item included, 0 = item excluded:

    [1, 0, 1, 1, 0, 1]  # Items 0, 2, 3, 5 selected

## Fitness Evaluation

- If weight ≤ capacity: fitness = total value
- If weight > capacity: fitness = total value - penalty * overage

## Usage

    iex> Jido.Evolve.Examples.Knapsack.run()
    # Evolution progress shown...
    # Final solution near optimal value

## Expected Results

Converges to near-optimal solution in 50-100 generations, demonstrating
how crossover combines good item selections from different parents.

# `batch_evaluate`

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

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

# `run`

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