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).
Phase 0.2 of the mutation-testing campaign. Adds a module gated on
#[cfg(feature = "async")] that, for every algorithm with a run_async,
asserts that the async runner produces the same result as the sync
runner given the same Config + seed + problem.
Before: nothing exercised run_async, so cargo mutants survived
'replace run_async body with OptimizationResult::new()' and every
comparison/arithmetic mutant inside the async loop for every
async-capable algorithm — about 25-30 algorithms * 5-10 mutants each.
After: every such mutant is killed because the parity test detects
any divergence in best.evaluation.objectives or pareto-front
objective tuples.
Coverage:
- Single-objective real (Sphere1D fixture): RandomSearch,
HillClimber, OnePlusOneEs, SimulatedAnnealing, GA, PSO, DE, CmaEs,
IpopCmaEs, sNES, TLBO, NelderMead, BayesianOpt, TPE.
- Multi-objective real (SchafferN1 fixture): NSGA-II/III, SPEA2,
MOEA/D, MOPSO, IBEA, SMS-EMOA, HypE, PESA-II, ε-MOEA, AGE-MOEA,
GrEA, KnEA, RVEA, PAES.
- Binary (OneMax): UMDA.
- Permutation (TinyTsp fixture): AntColonyTsp.
- Integer (AbsInt fixture): TabuSearch.
- Multi-fidelity (Sphere1DPartial fixture): Hyperband.
Run with: cargo test --features async --test algorithm_properties async_parity
Phase 0.1 of the mutation-testing campaign: a sweep test per algorithm
(33 total) asserting the exact strings returned by AlgorithmInfo::name()
and AlgorithmInfo::full_name() plus the seed propagated through
AlgorithmInfo::seed().
Before: cargo mutants survived dozens of mutants per algorithm replacing
the name/full_name return values with "" or "xyzzy", and the seed
return with None/Some(0)/Some(1). After: every such mutant is caught
by an exact-equality assertion.
NelderMead is deterministic and has no seed override (intentionally);
its test asserts seed() == None to pin the default-trait-impl behavior.
- Rewrites cookbook/permutation.md to cover the new operator toolkit:
initializers, crossovers (OX/PMX/CX/ERX), mutations, a 'what should I
use' picker, and a worked GA-on-TSP example. JSS multiset section
explains why the strict-permutation crossovers don't compose with
operation-string encodings and shows the local POX pattern.
- Adds cookbook/multi-objective-combinatorial.md: bi-objective TSP via
NSGA-II, bi-objective knapsack (binary encoding), 3-objective JSS
via NSGA-III, and a hypervolume-based operator comparison.
- Updates choosing-an-algorithm.md to reference the new operators in
the single- and multi-objective decision tables, plus a noting
NSGA-II/III's genericity over Vec<usize> and Vec<bool> decisions.
- SUMMARY.md and cookbook.md updated to list the new recipe.
tsp_operators_compare.rs runs NSGA-II four times on the KroAB-25
bi-objective TSP, holding everything constant except the crossover
operator. Ranks OX, PMX, CX, ERX by hypervolume against a fixed
reference point, plus front size, unique-point count, and runtime.
Pedagogical demonstration that the right comparison metric for a
Pareto search is hypervolume, not single-objective fitness.
Three harder Pareto-front demos:
- btsp_kroab.rs — Lust-Teghem bi-objective TSP (KroAB-25 subset of
TSPLIB KroA100/KroB100). NSGA-II with EdgeRecombinationCrossover +
InversionMutation. Reports hypervolume vs a fixed reference.
- mo_jss_la01.rs — 3-objective JSS on Lawrence LA01 (10x5 instance).
Objectives: makespan, total flow time, total tardiness (with
synthetic due dates dj = 1.3 * sum_processing_times(j)). NSGA-III
with reference_divisions = 12 (91 Das-Dennis points).
- mo_knapsack.rs — bi-objective 0/1 knapsack a la Zitzler-Thiele.
30 items, two profit vectors, one capacity. NSGA-II with a local
one-point binary crossover + BitFlipMutation; weight overruns
penalized in both objectives.
Two canonical combinatorial optimization benchmarks demonstrating the
new permutation toolkit:
- tsp_ulysses16.rs — single-objective TSP via GeneticAlgorithm using
OrderCrossover + InversionMutation. Reaches the known TSPLIB
optimum (6859) for the 16-city Ulysses GEO-distance instance.
- jss_ft06_bi.rs — bi-objective JSS (makespan + total flow time) on
the Fisher-Thompson 6x6 benchmark via NSGA-II using a local POX
crossover + SwapMutation. Makespan corner reaches the known
single-objective optimum (55).
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.
Pages is now enabled on the repo (Settings → Pages → 'Build and
deployment: GitHub Actions'), so the workflow can use the standard
configure-pages → upload-pages-artifact → deploy-pages chain
without needing the GITHUB_TOKEN to enable Pages itself.
PR builds run the build job (catches mdbook breakage) but skip the
deploy job, so PRs don't republish the live site.
Removes the heuropt-plot subcrate, the visualize example that used
it, and the related workspace plumbing (root [workspace] table, the
[workspace] override added to fuzz/Cargo.toml to detach from it,
heuropt-plot dev-dep, CHANGELOG mention).
The visualization concern is better served as an independent third-
party project than as a companion crate in this repo. No effect on
heuropt's public API or the async work in 0.8.0.
Two CI fixes; the previous `enablement: true` attempt didn't work
because the default GITHUB_TOKEN can write to Pages but can't enable
it on a repo that doesn't yet have it configured.
1. .github/workflows/docs.yml: drop the Pages deploy job entirely.
Build mdbook on every push and upload it as a CI artifact. When
Pages is enabled manually (Settings → Pages → 'Build and
deployment: GitHub Actions'), this file can grow back a deploy
job using actions/configure-pages + actions/deploy-pages.
2. fuzz/fuzz_targets/clamp_to_bounds.rs: the simplex projection's τ
computation operates on values up to `simplex_total · 1e6` per
the input filter, so its FP precision floor is ~1e-4 of the
input scale. Outputs near the `max(x_i − τ, 0)` clamp boundary
can flip between 0 and a small positive value across
re-applications without that being a correctness bug. The fuzz
target is meant to catch *gross* non-idempotence (the all-zeros
bug that the v0.4 cleanup fixed), not ULP-level slop. Loosen the
per-element tolerance to `1e-4 · max(simplex_total, max|x_i|, 1)`.
Verified clean over a 10 M-run soak.
The Docs workflow was failing on `actions/configure-pages@v5` with
"Get Pages site failed" because Pages isn't enabled on the repo
yet. Setting `enablement: true` lets the action auto-enable it so
the deploy can proceed without a manual Settings → Pages click.
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.
mdbook 0.4.40 (the version pinned in .github/workflows/docs.yml)
doesn't recognize edition = '2024' under [rust], failing the docs
build. Drop to '2021' for the in-book code blocks; the heuropt
crate itself stays on Rust 2024.
Adding [workspace] to the root Cargo.toml made fuzz/Cargo.toml
inherit it, but fuzz isn't in the members list — every fuzz-smoke
job failed with 'current package believes it's in a workspace when
it's not'. Add an empty [workspace] table at the top of
fuzz/Cargo.toml so cargo treats fuzz as the root of its own
workspace and stops walking up.
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.
cargo-fuzz's bundled Cargo.lock pinned rustix=0.36.5, which used the
now-removed `rustc_attrs` cfg name and broke the install step on
current nightly toolchain (the only toolchain that can build the
fuzzers via libfuzzer-sys). Letting cargo resolve fresh picks a
recent rustix that builds cleanly.
Fixes the fuzz-smoke matrix on the v0.4.0 push CI run.
CHANGELOG entry consolidates the unreleased work since v0.3.0:
testing-infrastructure expansion (proptest suites, cargo-fuzz
harness, stability tests, gungraun benches, CI), two real bug
fixes the testing surfaced (NaN-cycle non_dominated_sort, simplex
projection magnitude precision), the README decision-tree update
against the v0.3.0 comparison data, and the v0.4.0 perf pass
(cumulative compare harness 18.6 s → 5.7 s, 3.27×).
`examples/compare-results.md` refreshed with the post-perf-pass
ms numbers; quality metrics are bit-identical to the v0.3.0
snapshot (the perf pass was strictly CPU time, never algorithmic).
`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.
Cuts ~150 ms (-2.5 %) off the compare harness via better cross-crate
inlining of small Pareto/HV helpers. Costs ~20 s extra on a from-
scratch `cargo build --release`, but is essentially free on
incremental rebuilds.
Only applies when this crate is the workspace root (i.e. when
developing heuropt or running its own examples). Downstream users
who consume heuropt as a dependency see whatever profile their own
Cargo.toml configures.
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 %)
The compare harness (re-run on 2026-05-05 produced bit-identical
results to the v0.3.0 snapshot) doesn't square with four claims in
the DT. Adjust:
- BayesianOpt: was "gold standard". At 60 evals on 5-D Rosenbrock
with the default RBF kernel it produces f≈3172 (worse than
RandomSearch). Add the caveat that BO is the gold standard *with*
per-problem kernel tuning, not out of the box.
- MOPSO: was buried under "swarm style". On ZDT1 it wins HV outright
and beats every dominance-based method on convergence by ~100×.
Promote to its own "smooth real-valued 2-obj front" branch.
- SMS-EMOA: was "great on 2–3 obj at higher per-step cost". On these
benches it loses to NSGA-II on both ZDT1 (HV 102.9 vs 118.3) and
DTLZ2 (mean dist 0.048 vs 0.033). Reframe as "elegant in theory but
underperforms NSGA-II on these benches at our budgets".
- NSGA-III: was "strong default" for many-objective. On DTLZ1 (the
canonical linear-simplex test) it gets beaten by GrEA 3× and
MOEA/D 2×. Split the many-obj branch by front geometry: linear /
simplex → GrEA + MOEA/D; curved / unknown → NSGA-III + AGE-MOEA +
RVEA.
The quick-reference one-liners below the DT got the matching tweaks
so the table and the tree agree.
Goes from 10 properties to 50+, organized into four files:
- tests/properties.rs (existing) — Pareto-utility invariants
- tests/algorithm_properties.rs (new) — every Optimizer impl gets:
* determinism-with-seed property
* no-panic-on-random-valid-input property
* population-size-as-documented property where applicable
- tests/operator_properties.rs (new) — every Variation/Initializer/
Repair impl gets the right size + in-bounds + no-panic properties
- tests/metric_properties.rs (new) — every metric gets monotonicity
/ non-negativity / dim-checking properties
- tests/numerical_stability.rs (new) — single-point populations,
duplicate populations, near-zero bounds, very large bounds,
algorithms-on-flat-fitness — none of which should panic.
Total: 226 unit tests + this much-larger property suite. Strategies
are factored into a small `prop_helpers` module shared across files
so the random-input generators stay consistent.
Adds `.cargo/mutants.toml` configuring cargo-mutants to focus on the
algorithmic core (skipping benches, examples, tests_support) and pass
`--test-tool=cargo --no-shuffle` so a mutation that breaks the suite
gets caught quickly.
Mutation testing modifies the source one operator at a time (`>` →
`>=`, `+` → `-`, `true` → `false`, etc.) and re-runs the test suite.
A mutation that *survives* (tests still pass) is a hint that the test
suite isn't checking that bit of behavior — usually because:
- The mutated branch is dead code
- The unit tests rely on side-effects rather than return values
- A property test or invariant is missing
Not wired into CI as a gating check (it's slow — every mutation
re-runs the whole suite). Run locally with `cargo install cargo-mutants`
followed by `cargo mutants --in-diff HEAD~1` for incremental coverage,
or `cargo mutants` for a full sweep.
The config exclusions list explains *why* each module is skipped — most
are the "obvious" kind (benchmark harness, example problems) where
mutation kills are not informative.
Adds proptest as a dev-dependency and a `tests/properties.rs`
integration suite that probes invariants on randomly generated
inputs:
Pareto invariants:
- `pareto_compare` is anti-symmetric: A→B is opposite of B→A for
Dominates / DominatedBy
- `pareto_compare` is reflexive on equal candidates (returns Equal)
- `pareto_front` output is internally non-dominated
- `non_dominated_sort` puts every member into exactly one front
- `crowding_distance` returns Vec same length as front; boundary
points are infinity for fronts of size ≥ 2 in any axis-sortable
configuration
Operator invariants:
- `SimulatedBinaryCrossover` returns 2 children of the right length,
all in bounds
- `PolynomialMutation` returns 1 child of the right length, in bounds
- `BoundedGaussianMutation` returns 1 child in bounds
- `ClampToBounds` repair always lands in bounds
- `ProjectToSimplex` repair always sums to total and is non-negative
Algorithm invariants:
- For any seed, `Optimizer::run` is deterministic across two calls
- Final population has the documented size for population-based
algorithms
These are the invariants the existing 226 fixed-input unit tests
collectively check; proptest gives us coverage on inputs they don't
cover individually.
Wire `gungraun` 0.18 as a dev-dependency and a `benches/` directory
with instruction-count benchmarks for the algorithmic hot paths.
Why gungraun and not criterion: heuropt's hot paths are deterministic
numerical loops where wall-clock noise dominates real differences.
gungraun runs each benchmark under valgrind/callgrind once and reports
exact instruction counts — stable across machines and CI runners,
detects sub-microsecond regressions cleanly.
Benchmarks added:
- pareto::non_dominated_sort (the inner loop of every Pareto MOEA)
- pareto::crowding_distance (NSGA-II survival selection)
- metrics::hypervolume_nd (HSO recursion, used by SMS-EMOA)
- internal::cholesky (BO's per-step posterior factorization)
- algorithms::nsga2 single generation (end-to-end smoke check)
- algorithms::cma_es single generation (eigendecomposition cost)
Tracked size only — these aren't part of the regular CI matrix because
they need valgrind installed. Run with `cargo bench` locally.
Wired via the standard `[[bench]]` Cargo entries with `harness = false`
so gungraun's main_macro does the dispatch.
Snapshot of `cargo run --release --example compare` after the v0.3.0
algorithm cohort. The harness runs 7 benchmark problems × ~20
algorithms × 10 seeds each (≈3 minutes wall-clock); this file is the
reference output so readers can scan results without running it
themselves.
Highlights worth reading even if you're skipping the file:
- ZDT1: MOPSO and MOEA/D dominate convergence; (1+1)-ES and DE tie
at f = 0 on Rastrigin
- IPOP-CMA-ES drops vanilla CMA-ES from f=2.35 to f=0.13 on
Rastrigin (the multimodal failure-mode it was added to fix)
- IBEA wins DTLZ2 (15× closer to true front than NSGA-III)
- GrEA wins DTLZ1 (linear simplex front matches grid-based niching)
- Nelder-Mead = 0 exactly on Rosenbrock; CMA-ES at machine epsilon
- Bayesian optimization at 60 evals is honestly bad on 5-D problems
with the default kernel — flagged so readers don't conclude BO is
weak in general; it just needs more evals or hyperparameter tuning
The DT was written when v0.2.0 shipped. v0.3.0 added a whole regime
(expensive evaluation, multi-fidelity) plus new entries in existing
regimes (CMA-ES restart variant, smooth SO direct search, parameter-
free SO, etc.) — fold them in.
Specifically:
- New top-level branch on "how expensive is each evaluation?" so the
sample-efficient algorithms (BayesianOpt, Tpe) and multi-fidelity
ones (Hyperband) have a clear home.
- Continuous-SO branch gains IPOP-CMA-ES (multimodal), Nelder-Mead
(smooth, low-dim), (1+1)-ES (cheap baseline), sNES (high-dim
alternative to CMA-ES), Tlbo (parameter-free).
- Multi-objective branches gain SMS-EMOA, HypE, ε-MOEA, PESA-II,
AGE-MOEA, GrEA, KnEA, RVEA — placed by their distinguishing
characteristic (geometry-aware, knee-points, grid-based, etc.)
- Quick-reference table extended to all 35 algorithms and grouped by
paradigm.
CHANGELOG entry for the v0.3.0 cohort, version bump in Cargo.toml and
README. Theme: filling heuropt's expensive-evaluation and constraint-
handling gaps.
Algorithms (9 new): OnePlusOneEs, NelderMead, IpopCmaEs, BayesianOpt,
SeparableNes, Tpe, Hyperband.
Operators (1 new): LevyMutation. Repair operators (1 trait + 2 impls):
Repair<D> with ClampToBounds and ProjectToSimplex.
Selection helpers (1 new): stochastic_ranking_select.
Internal helpers: Cholesky factorization (used by BO).
API additions:
- CmaEsConfig.initial_mean: Option<Vec<f64>> (None preserves existing
midpoint-of-bounds behavior; used by IpopCmaEs to inject restart
diversity).
- New PartialProblem trait — multi-fidelity contract used by
Hyperband.
No breaking changes to v0.2.0 public API.
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).
Bergstra et al. 2011: sample-efficient sequential optimizer that's the
workhorse of Hyperopt and Optuna. Different surrogate from BO's
Gaussian process — TPE models p(x | y < y*) with one KDE and
p(x | y >= y*) with another, then samples candidates from the 'good'
KDE and ranks by the ratio l(x) / g(x). The acquisition is implicit
in the ratio (a closed-form analog of Expected Improvement).
Implementation:
- 1-D Gaussian KDE per axis, with bandwidth chosen by Scott's rule
- Per-step:
- Evaluate observations into 'good' (top γ fraction by target) and
'bad'
- Sample n_candidates from the good distribution (independent per
axis) and pick the one with the largest l(x)/g(x)
- Evaluate it, append to history
Vec<f64> only, single-objective only. Compared with BayesianOpt:
- Cheaper per-step (no GP factorization)
- Doesn't need kernel hyperparameter tuning to work well
- Naturally extends to mixed/categorical decision types (future work)
- Generally less sample-efficient than well-tuned BO on smooth
continuous problems, but more robust out of the box
Tests cover convergence on 1-D Sphere within a tight budget,
deterministic reruns, panic on multi-objective.
Wierstra et al. 2008/2014 NES with the diagonal-covariance "separable"
variant (sNES). Different theoretical foundation from CMA-ES: rather
than tracking a full covariance matrix and adapting it through
evolution paths, sNES updates the sampling distribution's parameters
by following the natural gradient of expected fitness.
Each generation:
- Sample λ offspring from N(μ, diag(σ²))
- Rank-shape the fitnesses (utility weights from the standard NES table)
- Update μ along the natural gradient: μ ← μ + η_μ · σ · sum(u_i · z_i)
- Update σ multiplicatively: σ_j ← σ_j · exp(η_σ/2 · sum(u_i · (z_i,j² - 1)))
Vec<f64> decisions only, single-objective only. The diagonal covariance
makes per-step cost O(λ·n) instead of CMA-ES's O(λ·n²) — much faster on
high-dimensional problems where full-covariance tracking is expensive
or numerically fragile, at the cost of being unable to handle strongly
rotated landscapes.
Adds runners for the four expensive-eval / gradient-free additions to
the appropriate single-objective sections of `examples/compare.rs`:
- Rastrigin (multimodal): now also shows IPOP-CMA-ES alongside vanilla
CMA-ES so the restart benefit is directly visible.
- Rosenbrock (smooth valley): adds Nelder-Mead (well-suited) and (1+1)
ES (cheap baseline).
- Ackley + Rosenbrock: BayesianOpt run with a deliberately TINY budget
(60 evaluations vs 30k for the population-based methods) so the
sample-efficiency claim is visible — BO with 60 evals vs DE/CMA-ES
with 30k.
The compare harness now sides-by-sides 23 algorithms total across the
seven benchmark problems.
The first sample-efficient algorithm in heuropt. Bayesian optimization
maintains a Gaussian-process surrogate of the objective and at each
step picks the next decision by maximizing an acquisition function on
that surrogate, so the evaluation budget is used surgically.
Implementation:
- **Kernel**: anisotropic RBF (squared-exponential) with per-axis
length scales, signal variance, and a small noise/jitter floor.
Hyperparameters are exposed in the config; a future version can add
marginal-likelihood maximization.
- **Posterior**: standard formulation. Cholesky factorizes K (using
the new internal helper); mean and variance predictions follow.
- **Acquisition**: Expected Improvement against the best observed
feasible point. Optimized by best-of-N random sampling — simple,
predictable cost, no inner-optimizer footgun.
- **Initial design**: `initial_samples` uniform-random points in
bounds before the BO loop starts.
- **Constraints**: feasibility-aware EI — best observed value uses
only feasible points; infeasible candidates are penalized.
Vec<f64> decisions, single-objective only. Targets the regime no
existing heuropt algorithm covers: 50–500 evaluations on an
expensive black-box function (CFD sim, ML training run, real-world
measurement).
Tests cover convergence on the 1-D sphere within a tight evaluation
budget (~30 evals get to f < 1e-6 — vs population-based methods
needing thousands), deterministic reruns, and panic on
multi-objective + dim mismatches.
Hand-rolled `A = L · L^T` factorization plus forward/backward triangular
solves, used by the upcoming Bayesian Optimization implementation for
the GP posterior. Same f64 row-major Vec<Vec<f64>> interface as the
existing Jacobi eigen helper so we don't pull in nalgebra for one
algorithm.
Returns Err on non-positive-definite input (a small jitter is the
typical caller-side fix). Tested against the standard 2x2 case, the
3x3 known-result case, A·x = b round-trip, and the SPD-failure case.
Auger & Hansen 2005 IPOP-CMA-ES: wraps the existing CmaEs in a restart
loop that doubles the population size and re-randomizes the mean
whenever a restart trigger fires. Specifically addresses the failure
mode we observed on Rastrigin (vanilla CMA-ES = 2.3 vs DE = 0).
Restart triggers:
- The whole budget for one inner CmaEs run finishes without improvement
- (More sophisticated triggers — eigenvalue collapse, condition-number
blow-up, sigma stagnation — are left for future versions; the
per-run budget trigger captures the bulk of the practical benefit)
Each restart:
- Doubles the population_size (Auger & Hansen 2005)
- Re-randomizes the initial mean to a fresh point in the bounds box
- Resets sigma to the user's initial value
Same Vec<f64> + single-objective constraints as CmaEs. The total
budget is divided across restarts; restart budget grows with
population. Tests verify it beats vanilla CMA-ES on Rastrigin.
Nelder & Mead 1965: gradient-free local optimizer that maintains a
simplex of n+1 points in n-D and at each iteration replaces the worst
vertex by one of {reflect, expand, outside-contract, inside-contract,
shrink} relative to the centroid of the rest. The five standard
coefficients (reflection α=1, expansion γ=2, contraction ρ=0.5,
shrinkage σ=0.5) are exposed in the config but default to canonical
values so users can leave them alone.
Single-objective only, Vec<f64> only, bounds enforced by clamping
each new vertex. Termination is purely iteration-count for v0.2;
"vertices have collapsed" stopping is a future enhancement.
Filling a real gap: heuropt had population-based local search
(SimulatedAnnealing, HillClimber) but no classical direct-search
algorithm. Excellent for low-dim smooth-ish problems where a
population is overkill.
Rechenberg 1973's elemental evolution strategy: one parent, one child
each generation, accept the child if it is no worse, and adapt the
mutation step size by tracking the success rate. If more than 1/5 of
recent moves were accepted the search is too cautious — multiply σ by
`step_increase` (typical 1.22). Below 1/5 — divide by the same factor.
At 1/5 — leave it alone. The success window has length `adaptation_period`.
Single-objective only. Vec<f64> only. Generic Gaussian step bounded by
the embedded `RealBounds`.
Why ship it: it's the smallest possible self-adapting evolution strategy
and a useful pedagogical / baseline endpoint. Pairs well as the budget
floor ("give me anything cheaper than CMA-ES").
Expands the comparison harness with four new test problems chosen for
their distinct geometry:
- **Rosenbrock** (single-obj, smooth valley): the classic non-convex
smooth function. Differentiates CMA-ES (which exploits the local
metric) from Rastrigin's multimodal-trap regime.
- **Ackley** (single-obj, exponential multimodal trap): a more
forgiving multimodal test than Rastrigin — fewer narrow local
minima — so CMA-ES can show its strength while DE/GA still win.
- **ZDT3** (multi-obj, disconnected front): the only ZDT-family
problem with a non-contiguous Pareto front. Tests an algorithm's
ability to maintain spread across gaps.
- **DTLZ1** (many-obj, 3-D linear front): a triangular plane in
objective space (vs DTLZ2's spherical octant). Different shape
reveals which many-obj algorithms are biased toward sphere-like
fronts vs which infer geometry adaptively.
Each new section runs all applicable algorithms × N seeds × the
algorithm-class budget the existing sections already use.
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.
Yang, Li, Liu & Zheng 2013 GrEA: many-objective MOEA whose secondary
ranking is a grid-based diversity score instead of crowding distance
or reference vectors.
Each generation:
- NSGA-II-like loop with offspring + non_dominated_sort
- For the splitting front:
- Translate by ideal/nadir; partition objective space into a
(`grid_divisions` per axis) grid
- For every member compute three grid scores:
- GR (grid rank) = sum of grid coordinates (closer to ideal = lower)
- GCD (grid crowding distance) = #neighbors within 1 grid unit (in any axis)
- GCPD (grid coordinate point distance) = max coord - min coord
- Sort F_l ascending by GR, then by GCD, then by GCPD
- Take the top `n - already_selected` survivors
GrEA's grid-based niching is a different lens from NSGA-III's reference
points and RVEA's reference vectors — particularly effective on
non-convex fronts where reference-vector approaches struggle.
Panichella 2019 AGE-MOEA: a many-objective MOEA that *infers* the
front's geometry (its L_p shape, where p = 1 is linear, p = 2 is
spherical, p < 1 is convex etc.) from the current non-dominated set
and uses that estimate to drive both proximity and diversity in
survival selection.
Each generation:
- NSGA-II-like loop: random parent selection + variation + evaluation
- Combine + non_dominated_sort
- Fill front-by-front; for the splitting front:
- Translate by ideal point z*
- Find extreme points by ASF (same as NSGA-III) and intercepts
- Estimate the geometry parameter p by minimizing
\|f − ideal\|_p constancy on the extreme points
- Score every member by survival_score = (proximity_to_ideal) +
(1 / nearest-neighbor distance in the same L_p frame)
- Keep the top scorers
The geometry estimation is the novel contribution; with 3+ objectives
it produces fronts whose spread better matches the true shape than
NSGA-III's reference points (which assume a known geometry).
Rao 2011 TLBO: parameter-free single-objective optimizer for Vec<f64>.
The selling point — uniquely among the metaheuristics we ship — is that
it has NO algorithm-specific hyperparameters: no F, CR, w, c1, c2, σ,
mutation rate, etc. Just population_size and generations.
Each generation has two phases:
- **Teacher phase**: identify the best individual (the 'teacher'). For
every learner, compute a 'mean' learner and try replacing it with a
candidate moved toward the teacher by a random fraction, scaled by
the gap between teacher and (TF · mean), where TF ∈ {1, 2}.
- **Learner phase**: each learner picks a random partner and tries
moving toward the better one of the pair. Only successful moves are
kept.
Single-objective only, Vec<f64> only, bounds enforced via clamping.
Tests cover Sphere1D convergence, deterministic reruns, and panic on
multi-objective.
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.
Adds runners for the five new MO algorithms in both the ZDT1 (2-obj)
and DTLZ2 (3-obj) sections of `examples/compare.rs`. The harness now
side-by-sides 11 multi-/many-objective optimizers (RandomSearch + 10
real ones) on each problem.
Replaces strict Pareto dominance with ε-dominance: A ε-dominates B when
`floor(A_i / ε) ≤ floor(B_i / ε)` for every objective and strictly
less in at least one (minimization frame). The result is a regular
discretization of objective space — at most one archive member per
ε-box — so the front spreads out automatically and the archive size
self-limits without truncation tricks.
Steady-state design: each generation samples one parent from the main
population and one from the ε-archive, applies variation, evaluates
the child, and offers it to both archives. Every member's
ε-coordinates and the box-tie rules are precomputed each insertion.
Tests: produces a front on Schaffer N.1 with reasonable spread,
deterministic reruns, panic on `epsilon[i] <= 0.0` and on
`epsilon.len() != objectives.len()`.
Corne, Jerram, Knowles & Oates 2001: divides objective space into a
hyperbox grid and uses per-box population counts to drive selection
toward sparsely-populated regions.
Each generation:
- Maintain an external archive of non-dominated members
- Build a hyperbox grid (`grid_divisions` per axis on the archive's
current axis ranges); count members per box
- Selection picks two parents by region-based tournament: choose two
random non-empty boxes and take a uniform-random member from the
one with fewer occupants
- Variation produces an offspring; insert into archive, dropping
dominated members and (if archive overflows) the most-crowded
occupant of the most-occupied box
Tests cover non-empty front on Schaffer N.1, deterministic reruns,
and panic on `archive_size == 0`.
Cheng, Jin, Olhofer & Sendhoff 2016 RVEA: many-objective MOEA built
around a fixed set of Das–Dennis reference vectors. Each generation:
- Generate offspring via random parent selection + variation +
evaluation
- Combine population + offspring; translate by ideal point z*
- Associate every member with the reference vector whose angle to
the translated objective vector is smallest
- For each occupied vector, keep the member with the smallest
Angle-Penalized Distance (APD) score; the rest are dropped
- APD = (1 + α(t)·θ_max·γ) · |f − z*| where γ is the angle to the
associated reference and α(t) = (t / t_max)^2 anneals the angle
penalty over the run
This produces well-spread fronts at high objective counts where
Pareto-rank methods (NSGA-II, SPEA2) lose discrimination.
Bader & Zitzler 2011: HypE estimates hypervolume contributions via
Monte Carlo sampling instead of computing them exactly. The point of
the trick is that exact hypervolume becomes prohibitively expensive
beyond ~5 objectives, while MC sampling stays cheap and accurate
enough at any dimension.
Each generation:
- Generate offspring via parent selection + variation + evaluation
- Combine, run non_dominated_sort, fill front-by-front
- For the splitting front, estimate each member's HV contribution
by drawing `n_samples` uniform points in the box [ideal, reference]
and counting how many points are dominated by *exactly* one front
member — that count, divided by n_samples and multiplied by the
box volume, is the member's expected unique HV contribution.
- Drop members one at a time from the splitting front by smallest
estimated contribution.
Public API matches the rest of the MO algorithms (Config + Optimizer).
The reference point is supplied in the config so the user controls
the integration domain. Tests cover non-empty front, deterministic
reruns, and panic on dim-mismatched reference.
Beume, Naujoks & Emmerich 2007: a steady-state MOEA that uses
hypervolume contribution as the secondary survival selection criterion.
Each generation:
- Generate ONE child via parent selection + variation + evaluation.
- Combine population + child, run non_dominated_sort.
- The discarded individual is the worst-front member with the
smallest hypervolume contribution (computed via the new
hypervolume_nd_from_evaluations helper).
Selection-quality is excellent at moderate objective counts (2–4) at
the cost of higher per-step compute (each survival selection requires
N+1 hypervolume evaluations of size ≤ N each). Best paired with a
tightly-bounded objective space — the user supplies a fixed reference
point in the config.
Tests: produces a non-empty front on Schaffer N.1, deterministic
reruns, panic on `population_size == 0`, panic on
`reference_point.len() != objectives.len()`.
Generalizes the existing 2-D hypervolume to arbitrary M ≥ 1 dimensions
using the standard recursive Hypervolume-by-Slicing-Objectives (HSO)
algorithm from While et al. 2006:
- For M = 1: return reference[0] - min(points[0])
- For M = 2: sort by axis 0, sweep accumulating rectangles (matches
hypervolume_2d's existing exact behavior)
- For M ≥ 3: sort by the last axis, peel off slices of increasing
thickness and recursively compute the (M−1)-dimensional HV of each
slice's projected non-dominated subset
Direction-aware: minimization-oriented input is the entry point, so
maximize objectives are negated by the caller via
`ObjectiveSpace::as_minimization` before the recursion runs.
Tested against:
- the existing 2-D analytical case (3 points → area 6)
- a known 3-D unit-cube case (1 point at origin, ref [1,1,1] → 1)
- empty front → 0
- agreement with hypervolume_2d on random 2-D fronts
A substantial README section walking newcomers through choosing an
optimizer. Defines the terminology as it comes up — single- vs multi-
vs many-objective, Pareto front, dominance, multimodality, evaluation
cost — so a reader who has never touched heuristic optimization can
still pick a sensible starting algorithm.
Five-step decision flow:
1. What is the decision?
2. How many objectives?
3. What's the landscape like? (multimodal, smooth, discrete)
4. How expensive is each evaluation?
5. Are there constraints?
Each branch ends with 1–3 algorithm recommendations and a one-line
rationale, plus a compact "quick reference" table at the bottom for
returning users.
Adds runners for HillClimber, SimulatedAnnealing, GeneticAlgorithm,
ParticleSwarm, CmaEs, and Umda to `examples/compare.rs`. Rastrigin
section now compares 8 single-objective optimizers against each other
on a fixed evaluation budget.
The MO sections (ZDT1, DTLZ2) are unchanged for now — MOPSO and IBEA
get added in a follow-up commit so each algorithm's debut shows up
clearly in the harness.
Mühlenbein 1997 UMDA: simplest Estimation-of-Distribution Algorithm for
`Vec<bool>` problems. Each generation:
- Evaluate the current population
- Select the top μ members by fitness
- Estimate per-bit marginal probability p_i = (count of 1s at bit i in
the μ-best) / μ
- Sample population_size new individuals from the resulting product-of-
Bernoullis distribution
Single-objective only. Bit-wise probabilities are clamped to
`[1 / (2 · μ), 1 - 1 / (2 · μ)]` to keep the population from collapsing
to a deterministic single string before convergence is meaningful
(standard Laplace-style smoothing for UMDA).
Tests: solves OneMax (maximize Σ bits) on a 20-bit instance,
deterministic reruns, panic on multi-objective.
Dorigo-style Ant System for permutation problems on a complete graph:
each generation, every ant constructs a tour by probabilistically
picking the next node from those it has not yet visited, weighted by
`τ_ij^α · η_ij^β` where τ is the pheromone level on edge (i, j) and
η is the heuristic desirability (1 / distance, here). After all ants
finish, pheromone evaporates by a factor `(1 - ρ)` and is reinforced
on each ant's tour proportional to that tour's quality.
Decision type is `Vec<usize>` (a permutation of 0..n_cities). The user
supplies a distance matrix and the n_cities is inferred. Single-objective
only (the cost is total tour length, which the Problem evaluates).
Tests build a 5-city ring and verify ACO finds a near-optimal tour,
plus deterministic reruns and panic on multi-objective.
Zitzler & Künzli 2004 IBEA: replaces Pareto-rank + crowding fitness
with a single scalar fitness derived from a binary quality indicator
(here, the additive ε-indicator). Loses no information at three or
more objectives the way crowding distance does.
Algorithm:
- For every (i, j) pair compute I(i, j) = max_k (f_k(i) - f_k(j)) on
minimization-oriented objectives.
- Fitness F(i) = -Σ_{j≠i} exp(-I(j, i) / κ).
- Each generation: combine parents + offspring, iteratively remove the
lowest-F member (cleanly recomputing the contribution of the dropped
member from each surviving member's fitness) until population_size
remain.
- Parent selection: binary tournament on F (higher wins).
Bounds-aware operators recommended (SBX + PolyMut).
Tests: produces a non-empty front on Schaffer N.1, deterministic
reruns, panic on `population_size == 0`.
Coello, Pulido & Lechuga 2004 MOPSO: PSO adapted for multi-objective
optimization via an external Pareto archive used as the source of
swarm leaders.
Each generation:
- Evaluate every particle's current position
- Insert non-dominated members into the archive (using ParetoArchive)
- For each particle, pick a leader from the archive (uniform random
among archive members)
- Update velocity using inertia + cognitive (toward pbest) + social
(toward leader)
- Update positions, clamp to bounds
- Refresh personal bests using Pareto comparison: pbest is replaced
only when the new position dominates it; on non-dominated, keep
with 50/50 random tiebreak
Vec<f64> decisions only. Truncates the archive to `archive_size` via
the existing simple-tail truncation. Tests: produces a non-empty
front on Schaffer N.1, deterministic reruns, panic on
single-objective.
Hansen & Ostermeier 2001 CMA-ES, the canonical real-valued
single-objective stochastic optimizer. Implements the full (μ/μ_w, λ)
update with rank-μ + rank-1 covariance updates and cumulative step-size
adaptation:
- Sample λ offspring from N(mean, σ² · C)
- Select the μ best, weight them, recompute mean
- Update evolution paths p_σ (step size) and p_c (covariance)
- Rank-1 update of C from p_c, plus rank-μ update from selected offspring
- Adapt σ via |p_σ| / E‖N(0,I)‖
Eigendecomposition (used to convert C into its B·D form for sampling
N(0, σ²·C)) goes through the new internal Jacobi helper, recomputed
every `eigen_decomposition_period` generations to amortize cost.
Vec<f64> decisions only. Bounds taken from a `RealBounds` field; mean
and offspring are clamped per dimension. Single-objective only.
Hyperparameters use the standard CMA-ES defaults (μ=λ/2, weights from
Hansen's tutorial, c_σ, c_c, c_1, c_μ, d_σ all formulae from §7.1).
Tests cover: convergence on Sphere1D and 5-D Rosenbrock, deterministic
reruns, panic on multi-objective, panic on `population_size < 4`.
Hand-rolled symmetric-matrix eigendecomposition via the cyclic Jacobi
rotation method. Returns sorted (eigenvalue, eigenvector) pairs in
descending order. Pure f64 row-major `Vec<Vec<f64>>` interface so we
don't pull in nalgebra for one algorithm.
Lives in `src/internal/eigen.rs` (new module). Used by the upcoming
CMA-ES implementation to maintain the covariance matrix's
eigendecomposition each generation. Tested against the standard
2x2 case, the diagonal case, and a known 3x3 result.
Glover 1986 tabu search for single-objective problems. Generic over
decision type — the user supplies a neighbor-generator closure that
produces a finite list of candidate moves from the current incumbent
(e.g. all 2-swaps for a permutation, or N Gaussian-perturbed copies of
a real vector). Each iteration picks the best non-tabu neighbor (with
an aspiration override that lets a tabu move through if it beats the
best-seen-ever incumbent) and adds the chosen move's decision to a
fixed-size FIFO tabu list.
Single-objective only. Tracks the best-seen-ever incumbent across the
run, returned as the result. Generic over the decision `D: Hash + Eq`
so the tabu list can match by full decision (simple and correct;
move-based tabu is left for users to implement themselves via a
custom decision wrapper).
Eberhart & Kennedy 1995 PSO with the standard inertia-weight update:
v[i,t+1] = w·v[i,t] + c1·r1·(pbest[i] - x[i,t]) + c2·r2·(gbest - x[i,t])
x[i,t+1] = clamp(x[i,t] + v[i,t+1], bounds)
Single-objective only, `Vec<f64>` decisions only (PSO's velocity vector
needs a Euclidean structure that doesn't generalize cleanly to bool/perm).
Velocities are clamped to ±(hi - lo) per dim to keep particles from
exploding off into space.
Config exposes the four standard knobs — swarm size, generations,
inertia w, cognitive c1, social c2 — plus a seed. Tests cover
convergence on Sphere1D, deterministic reruns, and panic on
multi-objective.
Canonical generational GA with elitism: each generation runs binary
tournament selection (using `tournament_select_single_objective`) on
the current population, applies the variation operator pair-wise to
produce offspring, evaluates them, then replaces the population while
preserving the top `elitism` members from the previous generation
(elitism prevents fitness regression on a single seed).
Single-objective only. Generic over decision type — pair with
`SimulatedBinaryCrossover + PolynomialMutation` for real-valued,
single-point crossover + bit-flip for binary, etc.
Tests: convergence on Sphere1D, deterministic reruns, panic on
multi-objective, panic on `population_size < 2`, panic on
`elitism > population_size`.
Classic Kirkpatrick et al. 1983 SA: hill climber that also accepts
worse moves with probability `exp(-Δ/T)` where T anneals geometrically
from `initial_temperature` to `final_temperature` over the iteration
count.
Single-objective only. Generic over decision type — works on real
vectors, bool vectors, permutations, anything. Tracks the best-seen
incumbent across the run (not just the last accepted move) so the
result reflects the actual best ever visited, not where the random
walk happened to end.
Tests cover: convergence on Sphere1D under reasonable hyperparameters,
deterministic reruns, panic on multi-objective, panic on
non-positive temperatures.
The simplest possible local search: start from one initializer-sampled
decision, repeatedly mutate it via the variation operator, and keep the
child only when it is strictly better than the current incumbent (with
the standard feasible-beats-infeasible / lower-violation tiebreaks
when relevant).
Single-objective only — panics with a clear message if the problem
exposes more than one objective. Deterministic under a seed. Returns
a population/front of size one (the current incumbent) so it slots
into the comparison harness like any other optimizer.
The Python tune_runtime.py treats the morning boot press and the 13:00
post-lunch re-tap as 'free' and only counts extra warning-phase taps.
That undercounts what the user actually presses each day and breaks
any comparison against a stated 'presses/day' comfort cap.
Updated `simulate_one` to count every press the user makes:
- boot press at workday start (always +1)
- 13:00 re-login press when the workday continues past lunch (+1)
- per-minute Bernoulli warning-phase presses (already counted)
- death-restart press: each time the device transitions running→dead
during workday and the user is at-desk (not at lunch), the user
presses to restart the cycle (warning press and death-restart for
the same cycle are mutually exclusive — extending via warning press
prevents that cycle's death)
With baseline now ~2 presses/day already mandatory, the hinge/cap
shift up too: PRESS_HINGE_LOW = 2.5/d (full reward up to baseline +
half a warning press) and PRESS_COMFORT_CAP = 3.5/d (rejected above).
Output 'Why' bullet now reports the total directly and notes the
component breakdown so the number is interpretable against the
new thresholds.
swaits