test(fuzz): add cargo-fuzz harness for Pareto and operator hot paths

fuzz/fuzz_targets/crowding_distance.rs

@@ -0,0 +1,50 @@
+#![no_main]
+//! Fuzz `crowding_distance` for shape and non-negativity.
+
+use arbitrary::Arbitrary;
+use libfuzzer_sys::fuzz_target;
+
+use heuropt::core::candidate::Candidate;
+use heuropt::core::evaluation::Evaluation;
+use heuropt::core::objective::{Objective, ObjectiveSpace};
+use heuropt::pareto::crowding::crowding_distance;
+
+#[derive(Arbitrary, Debug)]
+struct Input {
+    points: Vec<(f64, f64)>,
+}
+
+fuzz_target!(|input: Input| {
+    if input.points.len() > 64 {
+        return;
+    }
+    // Bound magnitudes — crowding's `(max - min)` and per-axis gaps can
+    // both overflow to +∞ when points span ±f64::MAX, yielding inf/inf=NaN.
+    if input
+        .points
+        .iter()
+        .any(|&(a, b)| !a.is_finite() || !b.is_finite() || a.abs() > 1e150 || b.abs() > 1e150)
+    {
+        return;
+    }
+    let space = ObjectiveSpace::new(vec![Objective::minimize("f1"), Objective::minimize("f2")]);
+    let pop: Vec<Candidate<()>> = input
+        .points
+        .iter()
+        .map(|&(a, b)| Candidate::new((), Evaluation::new(vec![a, b])))
+        .collect();
+
+    let front: Vec<usize> = (0..pop.len()).collect();
+    let d = crowding_distance(&pop, &front, &space);
+    assert_eq!(d.len(), front.len(), "crowding distance length mismatch");
+    for (i, &v) in d.iter().enumerate() {
+        assert!(v >= 0.0 || v.is_infinite(), "negative crowding[{i}] = {v}");
+        assert!(!v.is_nan(), "NaN crowding[{i}]");
+    }
+    // If size <= 2, every entry is +∞.
+    if pop.len() <= 2 {
+        for (i, &v) in d.iter().enumerate() {
+            assert!(v.is_infinite(), "size<=2 crowding[{i}] not inf: {v}");
+        }
+    }
+});