# Changelog All notable changes to this project will be documented in this file. The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). ## [Unreleased] ### Added - **`heuropt-plot` companion crate (v0.1.0)** at `heuropt-plot/`, published independently. Lightweight SVG-only plotter for Pareto fronts (`pareto_front_svg`) and convergence traces (`convergence_svg`) — hand-rolled SVG output, no `plotters` / `tiny-skia` dep so the crate stays a tiny optional addition. - `examples/visualize.rs` — runs NSGA-II on Schaffer N.1 with a closure observer that records hypervolume per generation, then emits `pareto_front.svg` + `convergence.svg` via `heuropt-plot`. ## [0.7.0] — 2026-05-05 Theme: async evaluation. heuropt now supports problems where each evaluation is a `.await`-able operation — HTTP services, RPC clients, spawned subprocesses. This is the differentiating capability vs. pymoo / hyperopt / MOEA Framework, none of which ship first-class async support. No public-API breaks for synchronous users. The new surface is gated behind a new `async` feature flag. ### Added - New optional feature `async`, gated on [`futures`](https://crates.io/crates/futures). - `core::async_problem::AsyncProblem` trait — mirrors `Problem` but with `async fn evaluate_async(&self, decision)`. Adapt an existing sync `Problem` with a one-line wrapper. - Per-algorithm `run_async(&problem, concurrency).await` methods on `RandomSearch` and `DifferentialEvolution` — drives evaluations through whichever async runtime the caller is using (typically tokio). `concurrency` bounds in-flight evaluations. - Internal `algorithms::parallel_eval_async::evaluate_batch_async` helper — uses `futures::stream::FuturesOrdered` with concurrency- bounded chunks, preserves input order so seeded determinism is preserved when evaluations are themselves deterministic. - `examples/async_eval.rs` — worked example with a simulated 20 ms remote service. At concurrency = 1 it's serial; at concurrency = 4 it's 2× faster; demonstrates DifferentialEvolution under tokio. [0.7.0]: https://github.com/swaits/heuropt/releases/tag/v0.7.0 ## [0.6.0] — 2026-05-05 Theme: production lifecycle. heuropt becomes deployable for long- running, real-world optimization workloads — callbacks, stop conditions, tracing, and two new performance indicators. No breaking changes to the public API. Existing `Optimizer

` impls keep compiling — `run_with` is added as a default-impl method that falls back to `run` plus a single final notification. ### Added #### Observer + stop-conditions API A new module `heuropt::observer` introduces: - `Snapshot<'a, D>` — per-generation observation payload with `iteration`, `evaluations`, `elapsed`, `population`, `pareto_front`, `best`, and `objectives`. - `Observer` trait — single method `observe(&Snapshot) -> ControlFlow<()>`. Closures of the right shape implement it automatically. `()` is the no-op observer. - `Optimizer::run_with(problem, observer)` — new method on the `Optimizer` trait with a default impl that falls back to `run`. Algorithms that override `run_with` (so far: `Nsga2`, `RandomSearch`, `DifferentialEvolution`) call the observer once per generation; others call it once at the end. Returning `ControlFlow::Break` halts the optimizer and returns the partial result. #### Built-in observers (`observer::builtin`) - `MaxTime(Duration)` — wall-clock cap. - `MaxIterations(usize)` — generation cap. - `TargetFitness(f64)` — direction-aware single-objective target. - `Stagnation { window, tolerance }` — halt when the best fitness hasn't improved by `tolerance` over `window` generations. - `Periodic::new(every, |snap| { … })` — call a user closure every `every` generations. - `AnyOf` / `AllOf` plus `Observer::or` / `Observer::and` for composition. - `TracingObserver` (behind the new `tracing` feature) — emits structured `debug!` events per generation. #### Tracing feature New optional feature `tracing`, gated on the [`tracing`](https://crates.io/crates/tracing) crate. Adds `TracingObserver` to the prelude when enabled. #### Performance indicators - `metrics::igd::igd` — Inverted Generational Distance against a reference set (typically the true Pareto front). - `metrics::igd::igd_plus` — Pareto-compliant IGD+ variant; adding a dominated point never improves the score. - `metrics::r2::r2` — R2 indicator using the weighted Tchebycheff utility. Pair with `pareto::das_dennis` for the canonical weight set. #### Constrained example `examples/constrained.rs` — solves the BNH constrained 2-objective problem (Binh & Korn 1996) with NSGA-II + the new observer API, demonstrating `Periodic` progress logging and `MaxTime` / composition. ### Changed - `Population::as_slice()` — new convenience accessor. [0.6.0]: https://github.com/swaits/heuropt/releases/tag/v0.6.0 ## [0.5.0] — 2026-05-05 Theme: comprehensive documentation and project polish. No public-API changes — bumping `heuropt = "0.5"` in your `Cargo.toml` is enough. ### Added #### User guide (mdbook) A new mdbook user guide at `docs/book/`, deployed to via a CI workflow on tag pushes. Chapters: - **Introduction** — what heuropt is, who it's for, what's in the box. - **Five-minute walkthrough** — install, define a problem, run an optimizer, look at the result. - **Defining a problem** — the `Problem` trait in depth: single- vs multi-objective, constraints, custom decision types (`Vec`, `Vec`, `Vec`, custom structs). - **Choosing an algorithm** — the README's decision tree, expanded to a full chapter with the reasoning behind every branch. - **Cookbook** — seven recipes covering parallelism, expensive evaluations, comparison harnesses, permutation problems, constraint repair, picking one answer off a Pareto front, and writing your own optimizer. - **Comparison with other libraries** — heuropt vs pymoo, hyperopt, optuna, MOEA Framework, metaheuristics-rs, argmin. Honest about when *not* to pick heuropt. - **Stability and SemVer** — explicit guarantees about which surfaces are stable; what's likely to change before 1.0; bit-identical determinism contract. - **Migration guides** — per-release upgrade notes. #### Runnable rustdoc examples Every algorithm now has a runnable ` ```rust ` example block in its rustdoc — 35 algorithms, all exercised by `cargo test --doc`. Plus the existing crate-level example in `lib.rs` and the `CompositeVariation` operator example. #### Real-world examples Three new polished examples covering distinct domains: - `examples/portfolio.rs` — multi-objective portfolio optimization with budget constraint via `ProjectToSimplex`. Pareto front of return-vs-risk trade-offs, plus a-posteriori weighted decision. - `examples/hyperparam_tuning.rs` — sample-efficient hyperparameter tuning with `BayesianOpt` and `Tpe`, demonstrating mixed-scale decoding (log-uniform learning rate, integer depth) and a 60-eval budget. - `examples/scheduling.rs` — single-machine weighted-completion-time scheduling: permutation decisions optimized via `SimulatedAnnealing` + `SwapMutation`, comparing against the Smith's-rule oracle. #### Governance docs - `CONTRIBUTING.md` — local-test checklist, conventional-commits requirement, contribution areas that land easily vs. those that need prior discussion. - `SECURITY.md` — disclosure policy, supported versions, what counts as a security issue. - `CODE_OF_CONDUCT.md` — adopts the [Builder's Code of Conduct](https://builderscode.org/) (CC0). - `.github/ISSUE_TEMPLATE/` — bug, feature, docs templates plus a `config.yml` that points security reports to the private vulnerability-disclosure flow. - `.github/PULL_REQUEST_TEMPLATE.md` — short, opinionated PR template. #### CI / tooling - `.github/workflows/docs.yml` — builds the mdbook user guide and deploys it to GitHub Pages on `main` pushes and tag pushes. ### Changed - README hero block expanded with badges and a punchier opening; added explicit links to the user guide, the docs.rs API reference, and the testing-coverage breakdown. - `lib.rs` crate-level docs polished — better intro, points readers at the user guide and the design spec. [0.5.0]: https://github.com/swaits/heuropt/releases/tag/v0.5.0 ## [0.4.0] — 2026-05-05 Theme: testing infrastructure, two real bug fixes surfaced by that infrastructure, and a CPU-time optimization pass that made the comparison harness 3.27× faster end-to-end. No breaking changes to the v0.3.0 public API. ### Performance A focused, measure-and-iterate optimization pass on the Pareto-based multi-objective hot paths. Every change verified bit-identical against the v0.3.0 comparison-harness snapshot — quality metrics (hypervolume, spacing, mean L2, mean dist, front size) match to the last decimal in every benchmark. **Cumulative wall-clock impact (compare harness, 10-seed mean):** | Algorithm / Problem | v0.3.0 | v0.4.0 | Speedup | |----------------------|--------:|--------:|--------:| | AGE-MOEA / DTLZ1 | 2299 ms | 229 ms | 10× | | SPEA2 / DTLZ2 | 4304 ms | 513 ms | 8.4× | | AGE-MOEA / ZDT3 | 932 ms | 193 ms | 4.8× | | NSGA-II / ZDT1 | 268 ms | 65 ms | 4.1× | | NSGA-II / ZDT3 | 267 ms | 65 ms | 4.1× | | SMS-EMOA / DTLZ2 | 5643 ms | 1369 ms | 4.1× | | NSGA-II / Rastrigin | 260 ms | 71 ms | 3.7× | | NSGA-II / DTLZ2 | 344 ms | 106 ms | 3.2× | | NSGA-III / DTLZ2 | 318 ms | 122 ms | 2.6× | | NSGA-III / DTLZ1 | 303 ms | 122 ms | 2.5× | | HypE / DTLZ2 | 80 ms | 44 ms | 1.8× | | **Total compare** | **18 629 ms** | **5688 ms** | **3.27×** | **Hot-path instruction counts (gungraun):** | Benchmark | v0.3.0 | v0.4.0 | Speedup | |-------------------------|------------:|---------:|--------:| | `hypervolume_nd_3d` n=100 | 13 523 760 | 367 767 | 37× | | `hypervolume_nd_3d` n=30 | 676 902 | 70 334 | 9.6× | | `non_dominated_sort_2d` n=200 | 13 513 271 | 2 601 813 | 5.2× | | `non_dominated_sort_2d` n=50 | 852 317 | 198 574 | 4.3× | | `spea2_short` | 179 113 | 133 783 | 1.34× | **Changes (in commit order):** - `perf(hypervolume)` — Rewrote the M≥3 HSO recursion in `hypervolume_nd`. The original cloned the active set into a fresh Vec> at the top of every recursive call, used a linear-scan `position` lookup to remove the just-processed point each band, and re-projected onto M-1 axes inside every band. Now: sort-by-index, pre-project once, slice prefixes for the active set, and skip `non_dominated_projection` when recursing into the M=2 base case (whose sweep already filters dominated points internally). - `perf(non_dominated_sort)` — Cache `as_minimization` / feasibility / violation per individual once at the top of the Deb fast-non-dominated-sort, then inline the dominance test against those arrays. The naïve formulation called `pareto_compare` twice per pair, each call allocating two fresh Vecs — 4N(N-1) allocations per sort. Propagates to every Pareto-based MOEA. - `perf(age_moea)` — Cache `lp_norm(translated[i], p)` once per candidate at function entry; maintain a `nearest[]` array updated incrementally on each pick (single `min` per remaining instead of a fresh full scan over the keep list). Cuts the splitting-front scoring loop from O(R · K · M) per iteration to O(R · M). - `perf(spea2)` — Two wins. (1) `compute_fitness` (called twice per generation): inline dominance against cached oriented arrays, symmetric distance matrix built once. (2) `build_archive` truncation: compute pairwise distances + sorted neighbor vectors once, then on victim removal use binary-search-remove on every survivor's still-sorted vector — total truncation cost O(K³ log K) → O(K² log K). - `perf(hypervolume)` — Index-sort instead of cloning point vectors in the M≥3 recursion. The N inner-Vec clones per HV call were redundant once we'd already sorted by last-axis. Big bench win (32×→37× cumulative on n=100/3D), modest wall-clock impact because SMS-EMOA's worst-front HV calls operate on small fronts. - `build(release)` — Enable thin LTO + codegen-units=1 in the release profile. Worth ~150 ms across the harness; only applies when heuropt is the workspace root, so downstream consumers see whatever profile their own Cargo.toml configures. - `perf(pareto_archive)` — Cache the candidate's oriented + feasibility once per `insert`, build each member's oriented vector once, and inline the two-pass dominance checks. Used by PESA-II (most impact), PAES, ε-MOEA, and any user code working through the archive directly. ### Added - **Decision tree update** in README to cover all v0.3.0 algorithms, with a new top-level branch on "is each evaluation expensive?" so `BayesianOpt` / `Tpe` / `Hyperband` have a clear home. - **Comparison results snapshot** at `examples/compare-results.md` — reference output of the harness across 7 benchmark problems and ~20 algorithms, captured after v0.3.0 landed. - **Instruction-count benchmarks** via `gungraun` (the Rust 2026 rename of `iai-callgrind`) at `benches/hot_paths.rs`. Covers `non_dominated_sort`, `crowding_distance`, `hypervolume_2d`, `hypervolume_nd` (HSO), and one-generation costs of NSGA-II and CMA-ES, plus a short-run bench for every algorithm. Stable across machines via callgrind. - **Property-based test suite expansion**: `tests/properties.rs` (Pareto-comparison antisymmetry, partitioning, operator bounds), `tests/algorithm_properties.rs` (per-algorithm determinism + population-size invariants — 32 tests, one per algorithm), `tests/operator_properties.rs` (every `Variation` / `Initializer` / `Repair` impl), `tests/metric_properties.rs` (HV / spacing invariants), and `tests/numerical_stability.rs` (empty / singleton / duplicate / flat-fitness / zero-width-bounds populations). - **Coverage-guided fuzz harness** at `fuzz/` (cargo-fuzz + libFuzzer). Eight targets covering `pareto_compare`, `non_dominated_sort`, `hypervolume_2d`, `ParetoArchive`, `crowding_distance`, `spacing`, SBX/PolyMut, and the `Repair` operators. Runs in CI for a short soak per PR; longer runs locally via `cargo +nightly fuzz run `. - **cargo-mutants config** at `.cargo/mutants.toml` for advisory mutation testing. Not gated in CI; run with `cargo mutants` to surface tests that don't actually check the behavior they look like they do. - **GitHub Actions CI** at `.github/workflows/ci.yml` with fmt / clippy / test (4-feature matrix) / doc / MSRV / fuzz-smoke jobs, all gated on `-D warnings`. ### Fixed - `pareto::sort::non_dominated_sort` previously dropped indices when the dominance graph contained a cycle (which arises when objectives contain NaN — `pareto_compare` becomes intransitive). Fuzzing the partition invariant surfaced the bug; orphans now go into a final residual front. - `operators::repair::ProjectToSimplex` could silently return the all-zero vector when the input vector's magnitude dwarfed `total` (the standard Duchi/Held-Wolfe τ computation lost precision and τ ≈ max(x), so `max(x_i - τ, 0)` rounded to zero everywhere). Detected by the `clamp_to_bounds` fuzzer; now falls through to a degenerate "all mass on argmax" projection above a 1e15 magnitude ratio, and is robust to floating-point precision loss in the algorithm's inner loop. [0.4.0]: https://github.com/swaits/heuropt/releases/tag/v0.4.0 ## [0.3.0] — 2026-05-05 Theme: filling heuropt's expensive-evaluation, gradient-free, and constraint-handling gaps. No breaking changes to the v0.2.0 public API. ### Added #### New algorithms (9) **Sample-efficient / surrogate-based:** - `BayesianOpt` — Gaussian-process Bayesian Optimization with Expected Improvement acquisition. heuropt's first sample-efficient algorithm: targets the 50–500 evaluation regime. - `Tpe` — Bergstra et al. 2011 Tree-structured Parzen Estimator (workhorse of Hyperopt and Optuna). KDE-based surrogate; cheaper per-step than BO and more robust without hyperparameter tuning. **Classical and modern evolution strategies:** - `OnePlusOneEs` — Rechenberg 1973 (1+1)-ES with the one-fifth success rule. Smallest possible self-adapting evolution strategy. - `IpopCmaEs` — Auger & Hansen 2005 increasing-population CMA-ES with restart. Specifically fixes vanilla CMA-ES's known weakness on multimodal problems. - `SeparableNes` — Wierstra et al. 2008/2014 Natural Evolution Strategy with diagonal covariance (sNES). Different theoretical foundation than CMA-ES; cheaper per-step at the cost of being unable to model rotated landscapes. **Direct search:** - `NelderMead` — Nelder & Mead 1965 simplex method. Classical gradient- free local optimizer; superb on low-dim smooth problems (Rosenbrock 5-D: f = 0 exactly). **Multi-fidelity:** - `Hyperband` — Li et al. 2017 multi-fidelity hyperparameter optimizer built on Successive Halving. Operates on a new `PartialProblem` trait so configurations can be evaluated at adjustable fidelity budgets. #### New operators - `LevyMutation` — heavy-tailed Lévy-flight mutation via Mantegna's algorithm. The actual algorithmic contribution from Cuckoo Search packaged as a reusable `Variation` operator. #### New traits + impls - `PartialProblem` — multi-fidelity problem contract: `evaluate_at_budget(decision, budget) -> Evaluation`. Used by `Hyperband`. Intentionally not a sub-trait of `Problem`. - `Repair` — in-place projection trait for restoring decisions to feasibility. Pair with `Variation` operators to get bounds-aware variants. Provided impls: - `ClampToBounds` for `Vec` per-axis clamping - `ProjectToSimplex` for L1-budget / probability-simplex projection #### New selection helpers - `stochastic_ranking_select` — Runarsson & Yao 2000 stochastic ranking. Better than strict feasibility-first tournament selection on heavily-constrained problems. #### Internal helpers - `internal::cholesky` — Cholesky factorization + triangular solves for SPD matrices, used by the GP posterior in `BayesianOpt`. ### Changed - `CmaEsConfig` gained `initial_mean: Option>`. `None` preserves the existing midpoint-of-bounds default; `IpopCmaEs` sets it to inject restart diversity without shrinking the search box. [0.3.0]: https://github.com/swaits/heuropt/releases/tag/v0.3.0 ## [0.2.0] — 2026-05-05 A substantial expansion of the algorithm catalog (21 new algorithms), five new operators, an n-D hypervolume utility, an algorithm-selection guide in the README, and a multi-seed comparison harness covering seven benchmark problems. No breaking changes to the v0.1.0 public API. ### Added #### New algorithms **Single-objective:** - `HillClimber` — simplest greedy local search. - `SimulatedAnnealing` — Kirkpatrick et al. 1983, generic over decision type. - `GeneticAlgorithm` — generational SO GA with tournament selection + elitism. - `ParticleSwarm` — Eberhart & Kennedy 1995 PSO for `Vec`. - `CmaEs` — Hansen & Ostermeier 2001 covariance-matrix adaptation. - `TabuSearch` — Glover 1986, with a user-supplied neighbor generator. - `AntColonyTsp` — Dorigo Ant System for permutation problems. - `Umda` — Mühlenbein 1997 univariate marginal-distribution EDA for `Vec`. - `Tlbo` — Rao 2011 Teaching-Learning-Based Optimization (parameter-free). **Multi-objective:** - `Mopso` — Coello, Pulido & Lechuga 2004 multi-objective PSO. - `Ibea` — Zitzler & Künzli 2004 indicator-based EA. - `SmsEmoa` — Beume, Naujoks & Emmerich 2007 S-metric selection EMOA. - `Hype` — Bader & Zitzler 2011 Hypervolume Estimation Algorithm. - `Rvea` — Cheng et al. 2016 Reference Vector-guided EA. - `PesaII` — Corne et al. 2001 Pareto Envelope-based Selection II. - `EpsilonMoea` — Deb, Mohan & Mishra 2003 ε-dominance MOEA. - `AgeMoea` — Panichella 2019 Adaptive Geometry Estimation MOEA. - `Grea` — Yang et al. 2013 Grid-based EA. - `Knea` — Zhang, Tian & Jin 2015 Knee point-driven EA. #### New operators - `BoundedGaussianMutation` — Gaussian noise + per-axis clamping. - `SimulatedBinaryCrossover` (SBX) — Deb & Agrawal 1995 canonical real-valued crossover. - `PolynomialMutation` — Deb's polynomial mutation, the standard NSGA-II pair to SBX. - `CompositeVariation` — pipeline two `Variation` operators (typically crossover → mutation). - `LevyMutation` — heavy-tailed Lévy-flight mutation via Mantegna's algorithm. #### New metrics / utilities - `hypervolume_nd` — exact N-dimensional dominated hypervolume via the Hypervolume-by-Slicing-Objectives (HSO) algorithm, plus an internal Jacobi symmetric eigendecomposition helper used by CMA-ES. #### New examples - `compare` — multi-seed comparison harness running every applicable algorithm across ZDT1, ZDT3, DTLZ1, DTLZ2 (multi/many-objective) and Rastrigin, Rosenbrock, Ackley (single-objective). Reports hypervolume, spacing, mean L2/dist, front size, and wall-clock ms. - `benchmarks` — canonical reference runs of NSGA-II on ZDT1 and DE on Rastrigin. - `jiggly_tuning` — real-world 4-objective NSGA-III firmware tuning for the [`jiggly`](https://github.com/swaits/jiggly) USB-mouse-jiggler, with an a-posteriori weighted-decision step that picks one recommendation off the Pareto front. #### New optional feature - `parallel` — rayon-backed parallel population evaluation in `RandomSearch`, `Nsga2`, `DifferentialEvolution`, `Spea2`, `Ibea`, `Mopso`, and most other algorithms with batchable inner loops. Seeded runs stay bit-identical to serial mode. #### Documentation - README gained an explanatory algorithm-selection decision tree that walks newcomers through choosing an optimizer, defining the terminology (multi-objective, Pareto front, dominance, multimodality, evaluation cost) as it goes. ### Changed - Minimum supported Rust version remains 1.85 (edition 2024). - Algorithm impls now require `P: Sync` and `P::Decision: Send` so the same impl serves both `parallel` and serial feature builds. Any `Problem` / decision type without exotic interior mutability already satisfies these. [0.2.0]: https://github.com/swaits/heuropt/releases/tag/v0.2.0 ## [0.1.0] — 2026-05-04 Initial release. ### Core types and traits - `Direction`, `Objective`, `ObjectiveSpace` (with `as_minimization` direction conversion). - `Evaluation` with feasibility (`constraint_violation <= 0.0`). - `Candidate`, `Population`, `OptimizationResult`. - `type Rng = rand::rngs::StdRng` and `rng_from_seed` so no public trait is generic over the RNG. - `Problem`, `Optimizer

`, `Initializer`, `Variation`. ### Pareto utilities - `pareto_compare`, `pareto_front`, `best_candidate`, `non_dominated_sort` (Deb fast non-dominated sort), `crowding_distance`, `ParetoArchive`, `das_dennis` (structured reference points for NSGA-III and MOEA/D). ### Operators - Real: `RealBounds`, `GaussianMutation`, `BoundedGaussianMutation`, `SimulatedBinaryCrossover` (SBX), `PolynomialMutation`. - Binary: `BitFlipMutation`. - Permutation: `SwapMutation`. - `CompositeVariation` pipeline (typically crossover → mutation). ### Selection helpers - `select_random`, `tournament_select_single_objective`. ### Reference algorithms - `RandomSearch` — sample-evaluate-keep baseline. - `Paes` — small (1+1) Pareto Archived Evolution Strategy. - `Nsga2` — canonical Pareto-based EA with crowding distance. - `Nsga3` — many-objective NSGA-III with reference-point niching. - `Spea2` — Strength Pareto Evolutionary Algorithm 2. - `Moead` — decomposition-based MOEA/D with the Tchebycheff scalar. - `DifferentialEvolution` — single-objective DE/rand/1/bin. ### Metrics - `spacing` (Schott), `hypervolume_2d` (exact 2-D dominated hypervolume). ### Examples - `random_search`, `toy_nsga2`, `custom_optimizer` — minimum-viable walkthroughs. - `benchmarks` — ZDT1 and Rastrigin reference runs. - `compare` — multi-seed comparison harness running every applicable algorithm on ZDT1 (2-obj), DTLZ2 (3-obj), and Rastrigin (single-obj), reporting hypervolume, spacing, mean L2, front size, and wall time. - `jiggly_tuning` — 4-objective NSGA-III tuning of the [`jiggly`](https://github.com/swaits/jiggly) USB-mouse-jiggler firmware with an a-posteriori weighted-decision step that picks one recommendation off the Pareto front. ### Optional features - `serde` — `Serialize` / `Deserialize` derives on the core data types. - `parallel` — rayon-backed parallel population evaluation in `RandomSearch`, `Nsga2`, and `DifferentialEvolution`. Seeded runs stay bit-identical to serial mode. [Unreleased]: https://github.com/swaits/heuropt/compare/v0.7.0...HEAD [0.1.0]: https://github.com/swaits/heuropt/releases/tag/v0.1.0