docs(examples): add toy_nsga2, random_search, and custom_optimizer

The three runnable examples called out in spec §18.5 / §19. All open
with `use heuropt::prelude::*;` so they double as a check that the
prelude is sufficient on its own:

- toy_nsga2.rs: Schaffer N.1 solved with NSGA-II.
- random_search.rs: 2D sphere solved with RandomSearch.
- custom_optimizer.rs: a minimal hill-climber implementing
  `Optimizer<P>` directly, demonstrating spec §2.3.

examples/random_search.rs

@@ -0,0 +1,36 @@
+//! Run a 2D sphere problem under `RandomSearch`.
+//!
+//! Run with:
+//!
+//! ```bash
+//! cargo run --example random_search
+//! ```
+
+use heuropt::prelude::*;
+
+struct Sphere2D;
+
+impl Problem for Sphere2D {
+    type Decision = Vec<f64>;
+
+    fn objectives(&self) -> ObjectiveSpace {
+        ObjectiveSpace::new(vec![Objective::minimize("f")])
+    }
+
+    fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
+        Evaluation::new(vec![x.iter().map(|v| v * v).sum()])
+    }
+}
+
+fn main() {
+    let initializer = RealBounds::new(vec![(-5.0, 5.0), (-5.0, 5.0)]);
+    let config = RandomSearchConfig { iterations: 500, batch_size: 1, seed: 7 };
+    let mut optimizer = RandomSearch::new(config, initializer);
+
+    let result = optimizer.run(&Sphere2D);
+
+    let best = result.best.expect("single-objective always has a best");
+    println!("Total evaluations: {}", result.evaluations);
+    println!("Best decision: {:?}", best.decision);
+    println!("Best f: {:.6}", best.evaluation.objectives[0]);
+}