The degenerate-magnitude shortcut in ProjectToSimplex::repair scans
`decision` for the argmax and concentrates all mass there. cargo
mutants found that the strict-greater scan was unpinned: replacing
`>` with `>=` (which would shift the argmax to the last tied
index) and `>` with `==` (which would silently skip larger
values further along) both survived.
Two tests:
- A 3-element vector with two tied maxima at the front pins that the
scan keeps the first index on a tie.
- A 3-element vector whose argmax is at index 1 pins that the scan
actually walks past the start when later values are larger.
Other mutants in this file (the `>` ↔ `>=` threshold check at line
106, the `*` ↔ `+` in the threshold constant, the `-` ↔ `+` /
`/` in the tau-fallback initializer, and the `>` ↔ `>=` in the
projection loop's rho update) are equivalent mutants for non-pathological
inputs: the normal-path and shortcut-path math converge to the same
projection result for any input the operator is documented to handle.
Leaving them in the residue.
Adds eight operators for permutation (Vec<usize>) decisions:
Initializers
- ShuffledPermutation { n } — random shuffles of [0..n)
- ShuffledMultisetPermutation { repeats_per_id } — random shuffles
of an arbitrary multiset (e.g., JSS operation strings)
Crossovers (strict permutations only)
- OrderCrossover (OX)
- PartiallyMappedCrossover (PMX)
- CycleCrossover (CX)
- EdgeRecombinationCrossover (ERX)
Mutations (preserve both strict permutations and multisets)
- InversionMutation
- InsertionMutation
- ScrambleMutation
All are re-exported from the prelude. Comprehensive unit tests + doctests
included; the pre-existing SwapMutation is untouched.
Completes the rustdoc audit — every public item now has at least one
```rust example block in its docstring, exercised by
`cargo test --doc` (55 doctests, all passing).
- Operators: BitFlipMutation, SwapMutation, RealBounds,
GaussianMutation, BoundedGaussianMutation,
SimulatedBinaryCrossover, PolynomialMutation, LevyMutation,
ClampToBounds, ProjectToSimplex.
- Metrics: hypervolume_2d, hypervolume_nd, spacing.
- Pareto utilities: pareto_compare, pareto_front, best_candidate,
non_dominated_sort, crowding_distance, das_dennis,
ParetoArchive.
Each example is short (5-15 lines) and self-contained — copy-paste
into a fresh project and it runs.
Spec §22 Round 4-D listed bounded mutation / repair operators as future
work; this is the second piece of that. A `Repair<D>` trait that nudges
infeasible decisions back to feasibility, intended to be called from a
user's Variation operator (or a CompositeVariation pipeline) when
projection-style constraint handling is preferred over the
penalty-style `constraint_violation` approach.
Trait:
pub trait Repair<D> {
fn repair(&mut self, decision: &mut D);
}
Provided impls:
- `ClampToBounds` — clamps each variable of a Vec<f64> to per-axis bounds
- `ProjectToSimplex` — projects a Vec<f64> onto the (clipped) probability
simplex (Σ x_i = total, x_i ≥ 0), useful for portfolio-style problems
and reference-direction normalization
Both stay in the existing `operators` module (alongside Variation
operators) since they share the same "transforms decisions" theme. Re-
exported from the prelude.
Zhang, Tian & Jin 2015 KnEA: many-objective MOEA that biases survival
selection toward 'knee points' on the Pareto front — points where a
small improvement in one objective costs a large degradation in
another.
Each generation:
- NSGA-II-like loop with offspring + non_dominated_sort
- For the splitting front, identify knee points by perpendicular
distance from the hyperplane connecting the front's extreme points.
Members further from the hyperplane (= more 'kneeness') are preferred.
- Survival keeps every knee-tagged member; if room remains, fill from
remaining members by largest perpendicular distance.
Knee points are intuitively the most attractive points on a Pareto
front when no preference information is available. KnEA pushes the
search toward them at the cost of less uniform front coverage.
Lévy-flight perturbation: each variable receives a step drawn from a
heavy-tailed Lévy(α) distribution rather than a Normal. The result is
"mostly small steps with rare big jumps," which gives a more
exploratory mutation than Gaussian without abandoning local search.
Decision type: Vec<f64>, with optional bounds (clamped per-axis if
`bounds` is non-empty). The step is sampled via Mantegna's algorithm
which generates Lévy(α) by combining two Normal samples and taking
the right power, controlled by the tail exponent `alpha` (typical
1.5; 1 is heavy, 2 collapses to Normal).
This is the only genuinely-different mutation kernel from Cuckoo
Search and other Lévy-flight metaheuristics; ship it as a Variation
operator usable from any algorithm rather than as a separate
algorithm.
- PolynomialMutation::vary: `#[allow(clippy::needless_range_loop)]`
on the per-dimension loop — body indexes both `self.bounds[j]` and
`child[j]` so a range index is the cleanest option.
- Operator tests: replace `x >= lo && x <= hi` with
`(lo..=hi).contains(&x)` per clippy's manual_range_contains lint.
Generic two-stage Variation operator: runs an inner crossover-style
operator on the parents, then applies an inner mutation-style operator
to each resulting child. Lets users build the canonical NSGA-II
operator stack — `SimulatedBinaryCrossover` followed by
`PolynomialMutation` — by composing the existing primitives instead
of bundling a one-off SbxPolyMut struct.
Lives in src/operators/composite.rs to keep type-specific operator
files unchanged. Generic over decision type and over both inner
operators.
Deb's standard real-valued mutation pair to SBX, used together by
canonical NSGA-II. For each variable, with probability
`per_variable_probability` (typical: 1/n where n is dim), perturb the
parent value by a polynomial-distributed delta scaled by the bound
range, then clamp.
Per-dim formula:
- `u ~ U[0, 1)`
- `δ = (2u)^(1/(η+1)) − 1` if `u < 0.5` else `1 − (2(1−u))^(1/(η+1))`
- `child[j] = parent[j] + δ · (hi − lo)`, clamped to bounds
`eta` is the distribution index (typical 20; smaller → more spread).
This is the simple bound-rescale form; the bound-aware δ_q variant from
the full paper is left as a future refinement.
Always returns one child. Tests cover: child stays in bounds with high
sigma-equivalent eta, per_variable_probability=0 returns the parent
unchanged, and standard panics.
Deb & Agrawal's standard real-valued crossover for NSGA-II. Takes two
parents, returns two children; per dimension, with
`per_variable_probability`, mixes the parents using a polynomial
spread parameter \\(\\beta\\) drawn from a distribution controlled by
`eta` (the distribution index — typical values 10–30, default 15).
Children are clamped to per-variable bounds.
Per-dim formula (Deb & Agrawal 1995):
- `u ~ U[0, 1)`
- `β = (2u)^(1/(η+1))` if `u ≤ 0.5` else `(1 / (2(1-u)))^(1/(η+1))`
- `c1 = 0.5·((1+β)·p1 + (1-β)·p2)`, `c2 = 0.5·((1-β)·p1 + (1+β)·p2)`
This is the simple compute-then-clamp form; the bounds-aware
β formulation from the full paper is left as a future refinement.
Tests cover: two children for two parents, output lengths preserved,
all variables clamped to bounds, and per_variable_probability=0
returns the parents unchanged.
A bounded variant of GaussianMutation: same Gaussian noise applied to
the first parent, but every variable is clamped to its per-dimension
inclusive bound. Useful as a drop-in for problems that need feasibility
maintained across generations rather than relying on
clamp-inside-evaluate.
Panics on `sigma <= 0.0`, on no parents, and on construction if any
`(lo, hi)` has `lo > hi`. Decision length must match the bounds
length when called.
- pareto/crowding.rs: rewrite the inner loop to iterate per-objective
via index_axis-style indexing on `oriented` rather than naming an
unused loop variable `k`.
- operators/{binary,permutation}.rs tests: pass parents via
`std::slice::from_ref` instead of `&[parent.clone()]` to avoid the
cloned_ref_to_slice_refs lint.
Pure cleanup — no behavior change, all 83 unit tests + 2 doctests still
pass.
Variation that clones the first parent (a Vec<usize> permutation) and
swaps two distinct random indices when len >= 2 (spec §11.4). Tests
confirm the multiset of contents is preserved.
Variation that clones the first parent and flips each bit independently
with probability `probability`. Panics if probability is outside [0, 1]
(spec §11.3).
Tests verify that probability=0 produces an unchanged child and
probability=1 flips every bit (spec §18.3).
`RealBounds` (Initializer<Vec<f64>>) samples each variable uniformly in
its inclusive (lo, hi) range; panics if any bound has lo > hi
(spec §11.1).
`GaussianMutation` (Variation<Vec<f64>>) clones the first parent and
adds Normal(0, sigma) noise to every element; panics on sigma <= 0.0;
does not enforce bounds in v1 (spec §11.2).
swaits