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