ERX had five tests, all checking `is_strict_perm` validity -- none
verified the *point* of edge recombination: that children actually
inherit parent edges. A "valid permutation but edge-ignoring" ERX would
have passed every existing test.
Adds:
- erx_identical_parents_inherit_every_edge: with identical parents the
child's edge set must equal the parent's exactly (zero foreign edges).
- erx_preserves_parent_edges_better_than_order_crossover: ERX must
strand fewer non-parent edges than Order Crossover -- a direct test of
ERX's reason to exist.
- erx_pinned_output: locks the adjacency-walk + min-degree tie-break.
Investigation result: ERX is correct and effective. It wins the
tsp_operators_compare showdown on KroAB-25 (hypervolume 638M vs OX 622M,
PMX 609M, CX 593M) and produces the most diverse front. The compare TSP
table's GA underperformance is an Order-Crossover-plus-generational-GA
artifact on a convex-position instance, not an ERX bug.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The acquisition loop ran acquisition_samples GP predictions per BO
iteration, each allocating three short-lived Vecs: the candidate point,
the k_star kernel vector, and solve_lower's output. Threading reused
buffers through new sample_uniform_in_bounds_into / predict_into /
solve_lower_into entry points removes ~3000 alloc/free pairs from
bayesian_opt_short.
bayesian_opt_short: 2_398_972 -> 2_255_904 (-6%). This is a structural
(allocation) win, not an algorithmic one -- the GP fit (Cholesky) and EI
prediction are inherently O(n^2)/O(n^3) with transcendental kernels, and
that work is unchanged. Output bit-identical -- all 606 tests pass,
including the run() snapshot; async builds clean.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Each TPE iteration drew `candidate_samples` candidates; every candidate
triggered three scott_bandwidths calls (one in sample_from_kde, two in
log_kde_density) -- each an O(support) two-pass scan plus a powf(-0.2). But
the good / bad supports are fixed for the whole iteration, so only two
distinct bandwidth vectors exist. Deriving them once and threading them
through cuts ~34 of every 36 scott_bandwidths calls. Also hoists the
constant (2*pi).sqrt() out of the inner density loop.
tpe_short: 382_518 -> 187_898 (-51%, 2.04x). scott_bandwidths is
deterministic in its inputs, so the once-vs-many results are identical --
output bit-identical, all 606 tests pass.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
build_tour computed pheromone[i][j].powf(alpha) and eta[i][j].powf(beta) for
every candidate at every step of every ant -- two transcendental calls per
edge consideration. But eta is constant for the whole run and pheromone is
constant across a generation's ant loop. Pre-raising eta to beta once and
pheromone to alpha once per generation (into a reused buffer) turns the hot
per-candidate weight into a single multiply.
ant_colony_tsp_short: 1_036_680 -> 539_924 (-48%, 1.92x). build_tour now
takes the pre-raised matrices; the three direct-call tests pre-raise via a
`raise` helper. Output bit-identical -- all 606 tests pass.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Both crossovers had an O(n) inner scan making them O(n^2): PMX located the
value to swap with `child.iter().position`, OX tested segment membership
with `segment.contains`. Both operate on strict permutations of 0..n, so a
value-indexed table (PMX: position kept in sync across swaps; OX: a static
membership bitmap) gives O(1) lookups. debug_asserts document the range
assumption, consistent with ERX and CX.
pmx_crossover_vary n=100: 21_507 -> 5_900 (-73%, 3.65x); n=30 -18%.
order_crossover_vary n=100: 18_265 -> 7_389 (-60%, 2.47x); n=30 -29%.
All four permutation crossovers are now O(n). Bit-identical for valid
permutations -- all 606 tests pass.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
cx_child located each cycle's next value with an O(n) `position` scan,
making the walk O(n^2). CX operates on strict permutations of 0..n, so a
direct value-indexed position table per parent gives O(1) lookups; a
debug_assert documents the range assumption (mirroring ERX).
cycle_crossover_vary n=100: 58_762 -> 12_084 (-79%, 4.86x); n=30 -36%.
Bit-identical for valid permutations -- all 606 tests pass.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The O(n^2) pair loop reads oriented[j] for every j; with Vec<Vec<f64>>
that chased a separate heap allocation per individual. A flat n*m buffer
keeps those reads contiguous and sequential in j.
non_dominated_sort_2d n=200: 1_288_072 -> 1_096_738 (-15%, 1.17x); n=50
-19%. nsga2 one-generation -5.7%. Combined with the earlier antisymmetry
fix, n=200 is down 55% from the original 2.46M. Pure data-layout change --
output bit-identical, all 606 tests pass.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The HSO M=3 path called the generic 2-D base case for every last-axis
slice, which re-sorted the active prefix by axis 0 each time -- O(n^2 log n)
overall. Since `projected` is already in last-axis order, sorting the
projected indices by axis 0 once and sweeping them with a `pi > k` skip
gives O(n^2) with no per-slice allocation. The M>=4 path is unchanged
(lifted out of the inner branch verbatim).
hypervolume_nd_bench_3d n=100: 361_595 -> 291_247 (-19%, 1.24x); n=30 -16%.
The sweep visits points in the same (axis-0, then last-axis) order the
stable per-prefix sort produced -- output is bit-identical, all 606 tests
pass.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
crowding_distance sorted bare front indices with a comparator that chased
two Vec<Vec<f64>> indirections per comparison. Extracting (objective value,
front position) tuples into a buffer reused across objectives keeps the hot
comparator a single f64 compare.
crowding_distance_2d n=200: 181_493 -> 173_286 (-4.5%); n=50 -4.5%. Stable
sort over the (value, index) pairs preserves the original tie-order, so the
output is bit-identical -- all 606 tests pass.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Edge Recombination Crossover scrubbed `current` from every one of the n
adjacency lists on each step of the walk -- an O(n^2) pass. The parent-tour
adjacency relation is symmetric (b in adj[a] iff a in adj[b]), so `current`
only ever appears in the lists of its own neighbors. Taking adj[current]
out with mem::take and retaining only over those lists is O(degree).
edge_recombination_crossover_vary n=100: 397_214 -> 140_403 (-65%, 2.83x);
n=30: 62_708 -> 39_987 (-36%). Output bit-identical -- all 606 tests pass.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The dominance relation is antisymmetric, so the outcome of compare(i, j)
fully determines compare(j, i). Iterating only j > i and applying the
result in both directions does identical work in half the pair scans.
non_dominated_sort_2d n=200: 2_461_178 -> 1_268_372 (-48%); n=50 -1.65x.
Ripples into dependents: nsga2 one-generation -20%, nsga3 / sms_emoa ~-9%.
Output is bit-identical (dominates[] still ascending, first_front order
unchanged) -- all 606 tests including run() snapshots pass.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The per-file Phase 1 test commits were written without running rustfmt
as I went; this pass formats the new test code (long assert_eq! lines
wrapped, etc.). Formatting-only — no behavioural change.
Phase 1, final algorithm batch:
- sms_emoa: pick_drop_index returns the singleton worst front, and
finds the least-HV-contributor at a non-zero index.
- pesa2: build_grid empty/corner-point boxing; region_tournament
prefers the less-crowded grid box (statistical majority).
- paes: deterministic non-empty front + archive cap.
- random_search: evaluation count = iterations*batch; best is no
worse than any sampled candidate.
Phase 1 tests:
- grea: environmental_selection truncates the 2N pool to exactly N
across three population sizes.
- hill_climber: full-run never-worsens and decreases-sphere pins.
- hype: binary_tournament picks the higher-fitness index (statistical
majority + valid-index invariant).
Phase 1 tests for the ε-MOEA box-archive helpers: per-axis floor in
box_coords, Euclidean corner_distance (incl. zero at exact corner),
and box_dominates across the strict/boundary cross-product.
Phase 1 tests for GA — feasibility-first fitness comparison across all
branches, and survival_selection's exact elite + best-offspring
composition (including the zero-elitism case).
Phase 1, tier 3 of the mutation-testing campaign — the shared Pareto /
metric / selection utilities used by every multi-objective algorithm.
A scoped cargo-mutants run found 75 survivors across these files; the
tests below target them.
- metrics/hypervolume.rs: dominates() boundary cases, non_dominated_
projection retained-set pins, hso_recursive 1-D/2-D base cases,
hypervolume_nd_from_evaluations empty/non-dominating skips.
- selection/tournament.rs: challenger_wins across the full feasibility
cross-product + equal-objective tie; better_by_objective and
better_by_feasibility branch pins; stochastic_ranking_select pf=0
feasibility ordering and count-wraps-modulo-population.
- pareto/crowding.rs: exact interior crowding distance on symmetric
and asymmetric fronts (pins the (next-prev)/span arithmetic).
- pareto/sort.rs: three-non-dominated-then-one-dominated and a strict
3-chain producing three singleton fronts.
- pareto/dominance.rs: trade-off → NonDominated, better-on-one-equal-
on-other → Dominates, identical → Equal.
- pareto/archive.rs: truncate boundary, trade-off kept alongside,
equal candidate rejected, smaller-violation infeasible eviction.
- pareto/front.rs: best_candidate keeps the first of tied minima.
- metrics/spacing.rs: exact spacing for a varying-NN-distance front.
src/core/problem.rs's lone survivor (decision_schema default body
'replace with vec![]') is an equivalent mutant — Vec::new() and vec![]
are identical — and is left in the residue.
Phase 1 tests for src/algorithms/cma_es.rs.
- compare_so: feasibility-first ordering, minimize/maximize inversion,
infeasibility-violation comparison.
- better_than_so: matches compare_so == Less; equal evaluations are
not strictly better.
- A 30-generation Sphere1D run pins that CMA-ES actually decreases
the objective (catches mutants that collapse the update rules).
Phase 1 tests for src/algorithms/bayesian_opt.rs. Adds 15 tests
pinning the GP regression and EI acquisition machinery:
- rbf_kernel: signal-variance return at zero distance, exp(-0.5) at
unit distance, monotone in length scale, decays to 0 for far points.
- normal_pdf: symmetric about zero, value at zero equals 1/sqrt(2π).
- normal_cdf: 0.5 at z=0, symmetric tail sums to 1.
- erf: odd function and erf(0) ≈ 0 within the rational approximation's
~1e-7 accuracy.
- expected_improvement: zero at sigma=0, monotone in sigma, positive
when mu < f_best.
- oriented_target: sign flips under direction, infeasible adds 1e6
penalty.
- better: feasibility-first then objective ordering under both
directions.
Phase 1 for src/algorithms/ant_colony_tsp.rs. Targets the ~30 remaining
mutants after Phase 0 sweeps — mostly arithmetic flips in build_tour
(pheromone × heuristic weighting) and the feasibility-comparison logic
in better_than_so.
Added:
- Four branch tests for better_than_so covering the full feasibility
cross-product: feasible-vs-infeasible (both orders), two-infeasible
(smaller violation wins), and two-feasible under both directions.
Plus an equal-objectives test pinning the strict-less-than semantics.
- build_tour-is-a-permutation invariant across 20 seeds × 6 start cities.
- A strong-heuristic test: with eta favoring the next-city by 1000x and
beta=5, build_tour walks the preferred path. Pins the .powf(beta)
arithmetic.
- Zero-alpha/zero-beta degenerate-case test: uniform random fallback
still returns a permutation.
Phase 1 for src/algorithms/age_moea.rs. Targets the ~50 algorithm-internal
mutants surviving after the Phase 0 sweeps:
Pure helper-fn pins (lp_norm / lp_distance / nearest_neighbor_distance /
estimate_p):
- L_p norm at p ∈ {1, 2} on canonical vectors (unit, all-ones,
Pythagorean 3-4-5, signed-via-abs).
- L_p distance: zero-to-itself = 0, symmetry, L_1/L_2 sanity values.
- nearest_neighbor_distance: empty selected → ∞, self-only → ∞, picks
the closest of mixed-distance candidates.
- estimate_p: empty-front fallback to p=2; axis-aligned extremes (CV=0
for all p, returns first candidate 0.25); corner-vs-diagonal extremes
(CV minimized at large p).
Full-run pins:
- A 10-generation Schaffer-N1 run with seed 7 verifies the pareto front
is non-empty and finite/nonneg (catches body collapse mutants).
- Population size after run matches config across three pop sizes
(catches size mutants).
- Evaluation count falls in [pop, pop*(gens+1)] (catches comparison
flips in the offspring-collection loop).
Phase 1 of the mutation-testing campaign for src/operators/real.rs (the
file with the largest mutant surface — 128 missed mutants spread across
GaussianMutation, BoundedGaussianMutation, SBX, PolynomialMutation,
LevyMutation, and the Mantegna gamma/sigma helpers).
Added:
- Seed-pinned numerical snapshots for each operator's vary() output on
a fixed parent and seed. Any arithmetic flip in the operator's math
changes one of the snapshot values and fails the assertion. The
snapshot tolerance is 1e-12 so even subtle FP drift is caught.
- An algebraic-identity test for SBX: c1 + c2 = p1 + p2 per dimension
before clamping. This identity holds for any β and pins the
(1+β)·p1 + (1-β)·p2 formula cleanly across 20 seeds.
- A scale-coupling test for PolynomialMutation: a 10× wider bound range
produces a 10× larger perturbation step at the same seed. Catches any
mutation that breaks the δ·(hi-lo) coupling.
- Three direct pin tests for mantegna_sigma_u (alpha = 1.5, 1.0, 2.0)
exercising the gamma() Lanczos series and the formula's edge cases
(alpha = 1.0 → Cauchy, alpha = 2.0 → Normal-limit where sin(π) ≈ 0).
- A monotonicity property test (sigma_u changes with alpha) to catch
structural mutants that collapse the formula to a constant.
Phase 1 of the mutation-testing campaign for src/operators/permutation.rs.
Adds 13 tests targeting the 30 surviving mutants in the new permutation
toolkit:
Mutation operators (Inversion / Insertion / Scramble):
- Previously only checked that the output was a valid permutation, which
passes trivially when the mutant 'replace >= 2 with < 2' skips the
guard entirely (no mutation = identity output = still a permutation).
New tests run 30 seeds on an 8-element parent and assert at least one
seed produces a non-identity output. Kills the >= ↔ < flips.
ShuffledMultisetPermutation::initialize:
- Tightened to assert pop.len() == size up-front, killing the 'replace
with vec![]' mutant.
Crossover operators (OX / PMX / CX / ERX):
- 'Recombines for n >= 3' tests: with 5-element distinct parents, some
seed must produce a child differing from both parents. Kills the
< ↔ > / == / <= guard flips that would early-return parents at n >= 3.
- CX-specific pinned tests: the single-cycle case (children = parents)
and the two-cycle case (exactly known output). Pins the cycle-detection
arithmetic and the parent-alternation logic — kills the ==↔!= and
+= ↔ *= mutants inside cx_child.
- ERX: 'distinct starts can yield distinct children' across 30 seeds —
kills the prev/next-index arithmetic mutants in the adjacency table.
Some residual mutants in this file are equivalent (e.g., < ↔ <= when
n=2 still produces the same OX result because for length-2 inputs the
segment-and-fill recombination converges to the parents anyway).
Documented in test comments.
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.
Phase 0.3 of the mutation-testing campaign. Extends the inline tests in
src/explorer/mod.rs with 24 new tests covering the gaps cargo-mutants
identified — about 30 surviving mutants in this one file.
Coverage added:
- Exact-output tests for ToDecisionValues impls on Vec<f64>, Vec<i64>,
Vec<bool>, Vec<usize> (the previous tests only asserted lengths or
spot-checked individual entries).
- from_result propagates evaluations / generations from the
OptimizationResult into RunMeta.
- with_problem_name / with_wall_clock / with_timestamp each set their
field and preserve the rest of the export.
- to_json emits a JSON containing schema_version, candidates, problem
name, and algorithm name strings.
- to_writer and to_file round-trip the same bytes.
- Free top-level to_json / to_writer / to_file convenience functions
exercised end-to-end (round-trip through tmp file).
- pad_decision_schema at all three boundaries (< target / == target /
> target) to pin the < comparison.
- candidate_to_export's in_pareto_front toggles at front_rank == 0.
- candidate_to_export's feasible toggles at constraint_violation <= 0.
- candidate_to_export pads short objective vectors and truncates long
ones (the defensive branch).
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.
Companion to the feat(explorer) commit. Bumps the version and
brings every cross-referencing doc up to v0.9 currency.
- Cargo.toml: version 0.8.0 -> 0.9.0.
- CHANGELOG: 0.9.0 entry covering the explorer export, the
Problem-side metadata additions, the AlgorithmInfo trait, the
pick_a_car example, and the new cookbook recipe.
- README: closing paragraph of the PickACar example points users
at the explorer with a one-call snippet
(`ExplorerExport::from_result(...).with_algorithm_info(...)
.to_file(...)?`). Version snippets bumped 0.8 -> 0.9.
- New cookbook recipe at docs/book/src/cookbook/explorer.md
covering: enabling the serde feature, enriching Problem with
labels/units/decision-schema, the export call, the JSON schema,
and custom decision-type handling.
- SUMMARY.md and cookbook.md link the new recipe.
- migration.md: new "To 0.9" section documenting the additive
changes (purely backwards-compatible upgrade from 0.8.x).
- introduction.md, comparison.md, choosing-an-algorithm.md,
stability.md: version refs bumped 0.8 -> 0.9.
- cookbook/parallel.md, cookbook/async.md: version refs bumped
0.8 -> 0.9.
- getting-started.md: version refs bumped, serde feature
description expanded to mention the explorer module.
- SECURITY.md: supported-versions table moves to 0.9.x.
Adds a tiny additive surface that turns any OptimizationResult into
a self-describing JSON file the heuropt-explorer webapp can load.
Real Pareto fronts have 50–200+ candidates spanning 2–7+ objectives;
reading them as numbers in a terminal scales badly. This commit
ships the heuropt-side of the explorer — the schema and the export
API. The webapp itself lives in a separate repo on its own cadence.
Three trait/type extensions, all with working defaults so existing
impls compile untouched:
- Objective gains optional `label: Option<String>` and
`unit: Option<String>` fields, plus fluent builders
`.with_label("Price").with_unit(\"\$k\")`. Existing
`Objective::minimize(name)` / `Objective::maximize(name)` are
unchanged. Both fields are #[serde(default,
skip_serializing_if = \"Option::is_none\")] so existing JSON
round-trips cleanly.
- Problem trait gains an optional
`fn decision_schema(&self) -> Vec<DecisionVariable>` with default
empty impl. Override it to provide pretty names / labels / units /
bounds for the explorer; the default produces fallback x[0],
x[1], … names. New DecisionVariable type at
`heuropt::core::DecisionVariable` with builder methods.
- New `heuropt::traits::AlgorithmInfo` trait with `name()`
(required) and `seed()` (default None). Every built-in algorithm
— all 33 — implements it. Separate from Optimizer<P> so
multi-fidelity Hyperband (which uses PartialProblem) implements
it uniformly.
The new explorer module:
- `heuropt::explorer::ExplorerExport` envelope with versioned
schema (SCHEMA_VERSION = 1).
- ExplorerCandidate per row, with front_rank from
non_dominated_sort attached at export time so downstream tools
don't re-derive it.
- ToDecisionValues adapter trait with provided impls for Vec<f64>,
Vec<bool>, Vec<usize>, Vec<i64>; custom decision types implement
one method.
- Free functions to_json / to_writer / to_file plus a builder API
(with_algorithm_info, with_problem_name, with_wall_clock,
with_timestamp).
- Gated on the existing `serde` feature, which now also pulls in
`serde_json` as a dep.
The example:
- `examples/pick_a_car.rs` — promotes the README's PickACar to a
real example, fully enriched with Objective labels/units and a
decision_schema. Runs NSGA-III for 200 generations, prints a
sample slice, writes pick_a_car.json. Gated on `serde`.
10 new explorer unit tests cover round-trip serde, fallback
decision-variable names, enriched export, AlgorithmInfo flow,
front-rank correctness, and the ToDecisionValues impls. Lib test
count went from 229 to 242.
Companion to the feat(async) commit. Brings every cross-referencing
doc up to v0.8 currency, replaces marketing-flavored copy with plain
prose, and replaces toy benchmark problems with relatable ones that
include actual run output and interpretive narrative.
- README: collapses the four-bullet "Read the user guide / API
reference / Tested with N tests / Hot paths optimized" list into
a single Docs links line.
- README: replaces the Schaffer-N1 toy problem with a PickACar
multi-objective design problem — three decision variables
(displacement, weight, drag), four objectives (price, 0-60,
fuel, noise), and *nonlinear* cost relationships so the Pareto
front is a real surface, not a 1D sweep. Includes actual NSGA-III
run output (representative slice across the 100-car front) and
a narrative explaining what each row tells you and why hand-
picking would miss the interesting tradeoffs.
- README: removes rustdoc-style hidden `#` setup lines from code
blocks. The README is rendered as plain markdown on GitHub /
crates.io, where those lines are visible garbage instead of
hidden setup. Code blocks are now self-contained.
- Guide quickstart (getting-started.md): replaces Sphere ( Σ x² )
with a least-squares LineFit example. Same shape (single-
objective continuous), but recognizable framing. Includes
actual CMA-ES output, residual table, and narrative comparing
the answer to standard regression.
- Algorithm count audit: stale "35 algorithms" claim corrected to
the actual 33 across README, src/lib.rs, introduction.md, and
the comparison.md table cell.
- Async feature flag listed in the optional-features sections of
README, src/lib.rs, getting-started.md.
- introduction.md, choosing-an-algorithm.md, comparison.md,
stability.md, migration.md, cookbook/parallel.md,
cookbook/custom-optimizer.md: cross-references updated to
describe full async coverage and link the new cookbook recipe.
- stability.md: removes the speculative "Observer / Snapshot /
Checkpoint planned" bullet (those didn't ship); documents the
AsyncProblem / AsyncPartialProblem trait stability.
- migration.md: new "To 0.8" section with paths from 0.5.x and 0.7.x.
- CHANGELOG: 0.8.0 entry capturing the async feature plus the
documentation / governance / CI catch-up.
- SECURITY.md: supported versions table reflects 0.8.x.
Async coverage was incomplete in 0.7 (only RandomSearch and
DifferentialEvolution had run_async). 0.8 closes the gap: every one
of the 33 algorithms now exposes
run_async(&problem, concurrency).await, gated on the async feature.
- Population-based algorithms fan out per-generation evaluations
through evaluate_batch_async with concurrency-bounded
FuturesOrdered chunks.
- Steady-state algorithms (HillClimber, SimulatedAnnealing,
OnePlusOneEs, Paes, NelderMead) await each step sequentially;
they accept the concurrency parameter for API uniformity.
- TabuSearch fans out the K-neighbor batch each step.
- Surrogate algorithms (BayesianOpt, Tpe) batch the initial design
and await per-iteration acquisitions sequentially so the surrogate
can update between picks.
- Hyperband uses a new AsyncPartialProblem trait (mirroring
PartialProblem for multi-fidelity workloads) and a parallel
evaluate_batch_at_budget_async helper; each Successive-Halving
rung fans out its budgeted evaluations.
All paths preserve seeded determinism: RNG draws happen on the main
task in the same order as the sync path, and only the evaluations
are concurrent.
Adds a dedicated cookbook recipe at docs/book/src/cookbook/async.md
with a worked example (DifferentialEvolution under tokio) and
guidance on picking concurrency. Cross-references in SUMMARY.md
and cookbook.md are updated to surface the new recipe.
The follow-up docs commit reconciles the rest of the user guide
and README to describe the new feature; this commit is the bare
async surface.
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.
Adds heuropt-plot, a tiny SVG-only plotter that takes heuropt
results and emits scatter plots (pareto_front_svg) and line plots
(convergence_svg). No heavy 'plotters' or 'tiny-skia' dep — hand-
rolled SVG so the crate adds <100 KB to a build.
Workspace setup: root Cargo.toml gains [workspace] with members =
['.', 'heuropt-plot']. heuropt-plot has its own version (0.1.0) and
publishes independently against heuropt 0.8+.
Adds examples/visualize.rs that wires it up: NSGA-II on Schaffer
N.1, plain run() (no observer plumbing), final-front SVG written to
disk.
Adds the headline async/await capability for IO-bound evaluations
(HTTP services, RPC clients, spawned subprocesses) — the
differentiator vs pymoo / hyperopt / MOEA Framework.
No public-API breaks for synchronous users. The new surface is
gated behind a new `async` feature flag.
- core::async_problem::AsyncProblem trait (async fn evaluate_async).
- algorithms::parallel_eval_async::evaluate_batch_async helper using
futures::stream::FuturesOrdered with concurrency-bounded chunks;
preserves input order so seeded determinism holds when evaluations
are themselves deterministic.
- run_async on RandomSearch and DifferentialEvolution.
- examples/async_eval.rs: simulated 20 ms remote service. concurrency=1
→ 4.2 s, concurrency=4 → 2.1 s (2× speedup).
Bumps Cargo.toml to 0.8.0; CHANGELOG entry covers the above plus a
note that 0.6.0/0.7.0 on crates.io are yanked experimentals and 0.8
picks up cleanly from 0.5.
Theme: documentation and project polish. No public-API changes; this
is the v0.5 release that elevates heuropt's docs/onboarding/governance
to bar-setting status.
Adds:
- mdbook user guide at docs/book/ with intro, getting-started,
defining-problems, choosing-an-algorithm, cookbook (7 recipes),
comparison vs other libraries, stability/SemVer, migration guides.
Deploys to https://swaits.github.io/heuropt/ via .github/workflows/
docs.yml.
- Runnable rustdoc examples on every algorithm (35 of them), all
exercised by cargo test --doc.
- Three real-world examples: portfolio.rs (multi-obj with budget
constraint), hyperparam_tuning.rs (BO + TPE), scheduling.rs
(permutation via SA + SwapMutation against Smith's-rule oracle).
- Governance: CONTRIBUTING.md, SECURITY.md, CODE_OF_CONDUCT.md
(adopting builderscode.org's Builder's Code of Conduct), GitHub
issue templates, PR template.
Polishes:
- README hero with badges + user-guide link.
- lib.rs crate-level docs.
- CHANGELOG entry for 0.5.0.
Bumps Cargo.toml to 0.5.0.
`ParetoArchive::insert` calls `pareto_compare` twice per existing
member (once per pass), and each call re-allocates two Vec<f64>s
via `as_minimization` — 4N allocations per insert. Cache the
candidate's oriented + feasibility/violation once, build each
member's oriented vector once for the call, then inline the
dominance test against those cached arrays.
Used by PESA-II (per offspring per generation), PAES (per child),
ε-MOEA, and any user code working through the archive directly.
Wall-clock (compare harness, 10-seed mean):
- PESA-II / DTLZ2: 498 → 426 ms (-14 %)
- PESA-II / ZDT1: 87 → 75 ms (-14 %)
Smaller wins on PAES / MOPSO / IBEA / HypE / ε-MOEA where the
archive isn't the dominant per-generation cost.
Bit-identical via the compare harness.
The M≥3 branch of `hso_recursive` cloned every input point into
`sorted: Vec<Vec<f64>>` solely so it could sort. Each clone is M
f64s allocated; with N points per call and ~30 HV calls per SMS-EMOA
generation × 30 k generations, that's millions of small Vec<f64>
allocations.
Sort indices into a `Vec<usize>` instead, then iterate the original
points by index. The pre-projection step still produces a
Vec<Vec<f64>> (which the active-prefix slicing requires), but we
save the outer N inner-Vec clones per call.
gungraun (instructions):
- hypervolume_nd_3d n=30: 87 969 → 70 334 (-20 %, 1.25×)
- hypervolume_nd_3d n=100: 422 767 → 367 767 (-13 %, 1.15×)
Cumulative vs the v0.3.0 baseline:
- hypervolume_nd_3d n=30: 676 902 → 70 334 (9.6×)
- hypervolume_nd_3d n=100: 13 523 760 → 367 767 (37×)
Wall-clock impact is in the noise on the compare harness because the
SMS-EMOA worst-front HV calls operate on small fronts (5–10 points
once converged). The win is most visible in synthetic dense-front
HV benchmarks.
Two independent wins in SPEA2's per-generation hot path. Both
bit-identical against the compare harness.
# 1. compute_fitness — cache oriented + distance matrix
`compute_fitness` is called twice per generation. The strength-graph
loop calls `pareto_compare` in an N² loop, allocating two Vec<f64>s
per call via `as_minimization`. Inline the dominance test against
cached oriented arrays. The density loop's per-row euclidean recompute
is replaced by a symmetric N×N distance matrix built once.
# 2. build_archive — incremental sort maintenance in truncation
The archive-truncation loop was O(K³ log K) — each pruning iteration
recomputed every alive member's pairwise distances and re-sorted them,
when the only change since the prior iteration was that one specific
neighbor (the just-removed victim) became dead. Compute the distance
matrix and sorted neighbor vectors once, then on victim removal use
binary-search-remove on every survivor's still-sorted vector. Total
truncation cost drops from O(K³ log K) to O(K² log K). Victim choice
is bit-identical.
gungraun (instructions):
- spea2_short: 179 113 → 133 783 (-25 %, 1.34×)
Wall-clock (compare harness, 10-seed mean):
- SPEA2 / ZDT1: 458 → 241 ms (1.9×, cumulative)
- SPEA2 / DTLZ2: 4304 → 513 ms (8.4×, cumulative)
The splitting-front survival selection in AGE-MOEA recomputed two
expensive things per while-iteration:
* `lp_norm(translated[i], p)` for every remaining i — even though the
value is constant across iterations.
* `nearest_neighbor_distance(i, …, &keep, p)` — a fresh full scan
over the keep list, even though only one new candidate was added
since the last scan.
Both are `powf`-heavy in the L_p frame.
Compute lp_norm once per candidate at function entry. Maintain a
`nearest[]` array seeded from the initial keep set and updated on
every pick by a single `min(nearest[i], lp_distance(i, pick, p))`
per remaining i. That cuts the score loop from O(R · K · M) to
O(R · M) per iteration, with the dominant powf calls in
lp_distance counted once per (remaining, pick) pair instead of per
(remaining, full-keep).
Wall-clock (compare harness, 10-seed mean):
- AGE-MOEA / DTLZ1: 2266 → 430 ms on top of v0.3.0 baseline (5.3×)
- AGE-MOEA / ZDT3: 935 → 376 ms (2.5×)
The Deb fast non-dominated sort calls `pareto_compare` twice for
every (i, j) pair, and each `pareto_compare` call invokes
`ObjectiveSpace::as_minimization` twice — so for an N-point
population that's 4·N·(N-1) fresh `Vec<f64>` allocations per sort.
At N=100 with thousands of generations across the compare harness,
this dominated the per-generation cost of every Pareto-based MOEA.
Cache `as_minimization`/feasibility/violation once per individual
up front, then inline the dominance test against those cached
arrays. The output (per-pair dominance outcome and the per-i
`dominates` lists) is bit-identical to `pareto_compare`.
gungraun (instructions):
- non_dominated_sort_2d n=50: 852 317 → 198 574 (-77 %, 4.3×)
- non_dominated_sort_2d n=200: 13 513 271 → 2 601 813 (-81 %, 5.2×)
Wall-clock (compare harness, 10-seed mean):
- NSGA-II / ZDT1: 268 → 65 ms (4.1×)
- NSGA-II / ZDT3: 267 → 65 ms (4.1×)
- NSGA-II / DTLZ2: 344 → 106 ms (3.2×)
- NSGA-II / Rastrigin: 260 → 71 ms (3.7×)
- NSGA-III / DTLZ2: 318 → 122 ms (2.6×)
- NSGA-III / DTLZ1: 303 → 122 ms (2.5×)
- SMS-EMOA / DTLZ2: 1413 → 1369 ms (small additional win on top of HV)
- AGE-MOEA / DTLZ1: 430 → 229 ms (1.9×, on top of the AGE-MOEA caching)
- HypE / DTLZ2: 80 → 44 ms (1.8×)
The HSO recursion in `hypervolume_nd` had three overheads that
dominated SMS-EMOA's per-generation cost on DTLZ2 (5.6 s baseline,
~30 k generations × ~40 HV calls per generation = ~1.2 M HV calls
per run):
1. `active = sorted.clone()` plus `active.iter().position(...)`
linear scan to remove the just-processed point each band — O(N)
per band, total O(N²) per HV call.
2. Per-band re-projection
`active.iter().map(|q| q[..last].to_vec())` — full
Vec<Vec<f64>> rebuild for every band, O(N·M) allocations per HV
call.
3. `non_dominated_projection` called even when recursing into the
M=2 base case, whose sweep already filters dominated points
internally.
Replace (1) with prefix-slicing `projected_all[..=k]` (sort points
ascending by last axis once; the active set at each band is just a
prefix). Pre-project once outside the loop (2). Skip the explicit
non-dominance filter when the inner recursion is M=2 (3).
Bit-identical output verified by re-running the compare harness and
diffing against the v0.3.0 snapshot — every quality metric matches
to the last decimal.
gungraun (instructions):
- hypervolume_nd_3d n=30: 676 902 → 87 969 (-87 %, 7.7×)
- hypervolume_nd_3d n=100: 13 523 760 → 422 767 (-97 %, 32×)
Wall-clock (compare harness, 10-seed mean):
- SMS-EMOA / DTLZ2: 5643 ms → 1413 ms (-4230 ms, -75 %)
Multi-fidelity optimization. Hyperband (Li et al. 2017) and its
foundation Successive Halving (Karnin et al. 2013) tune
hyperparameters by allocating *uneven* compute across configurations:
sample many cheap-to-evaluate-at-low-budget configs, then promote
the survivors to higher budgets. Crucial for ML hyperparameter
tuning where each evaluation is a partial training run.
This requires a new trait — `Problem::evaluate` is a single-shot
black box, but Hyperband needs to evaluate the SAME decision at
different fidelity budgets:
pub trait PartialProblem {
type Decision: Clone;
fn objectives(&self) -> ObjectiveSpace;
fn evaluate_at_budget(&self, decision: &Self::Decision,
budget: f64) -> Evaluation;
}
`PartialProblem` is intentionally NOT a sub-trait of `Problem`.
Implementors who already have a `Problem` and want their
`evaluate_at_budget` to ignore budget can write a one-line wrapper.
`Hyperband` is the optimizer:
pub struct HyperbandConfig {
max_budget: f64, eta: f64, max_brackets: usize, seed: u64,
}
pub struct Hyperband<I> { config, initializer, ... }
Single-objective only. The decision sampler is an `Initializer<D>` so
it works the same way as every other heuropt algorithm. Generic over
decision type.
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.
Runarsson & Yao 2000 stochastic ranking: a probabilistic alternative
to feasibility-first tournament selection. Each pairwise comparison
during a bubble-sort pass uses the *objective* value with probability
`pf` even when one or both candidates are infeasible. The classic
recommendation `pf = 0.45` reliably outperforms strict
feasibility-first on heavily-constrained problems where occasionally
exploring the infeasible region helps cross narrow feasible corridors.
New helper: `stochastic_ranking_select` lives next to
`tournament_select_single_objective` in `selection::tournament`.
Single-objective only; same signature pattern (population, objectives,
count, rng, plus the new `pf` knob).
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