docs(0.9): release notes, cookbook recipe, README polish
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.
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@@ -28,7 +28,7 @@ hyperopt, optuna, DEAP). heuropt's design priorities:
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1. **Approachable code.** No trait objects in the public API. No
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GATs, HRTBs, generic-RNG plumbing. A junior Rust engineer should
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be able to read `RandomSearch` and write a new optimizer by
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be able to read Random Search and write a new optimizer by
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implementing only the `Optimizer<P>` trait.
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2. **One concrete RNG type.** Seeded determinism is a property tested
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across the crate; identical inputs always produce identical
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@@ -44,20 +44,18 @@ hyperopt, optuna, DEAP). heuropt's design priorities:
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## What's in the box
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heuropt v0.8 ships **33 algorithms** spanning:
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heuropt v0.10 ships **33 algorithms** spanning:
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- Single-objective continuous: `RandomSearch`, `HillClimber`,
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`OnePlusOneEs`, `SimulatedAnnealing`, `GeneticAlgorithm`,
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`ParticleSwarm`, `DifferentialEvolution`, `Tlbo`, `CmaEs`,
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`IpopCmaEs`, `SeparableNes`, `NelderMead`.
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- Single-objective other types: `Umda` (binary), `TabuSearch`
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(any), `AntColonyTsp` (permutation).
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- Multi-objective (2–3): `Paes`, `Nsga2`, `Spea2`, `Mopso`, `Ibea`,
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`SmsEmoa`, `HypE`, `EpsilonMoea`, `PesaII`, `AgeMoea`, `Knea`,
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`Moead`.
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- Many-objective (4+): `Nsga3`, `Rvea`, `Grea`.
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- Sample-efficient / multi-fidelity: `BayesianOpt`, `Tpe`,
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`Hyperband`.
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- Single-objective continuous: Random Search, Hill Climber,
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(1+1)-ES, Simulated Annealing, GA, PSO, Differential Evolution,
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TLBO, CMA-ES, IPOP-CMA-ES, sNES, Nelder-Mead.
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- Single-objective other types: UMDA (binary), Tabu Search (any),
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Ant Colony (permutation).
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- Multi-objective (2–3): PAES, NSGA-II, SPEA2, MOPSO, IBEA,
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SMS-EMOA, HypE, ε-MOEA, PESA-II, AGE-MOEA, KnEA, MOEA/D.
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- Many-objective (4+): NSGA-III, RVEA, GrEA.
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- Sample-efficient / multi-fidelity: Bayesian Optimization, TPE,
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Hyperband.
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Plus the operators (SBX, PolynomialMutation, BoundedGaussianMutation,
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LevyMutation, BitFlipMutation, SwapMutation, ClampToBounds,
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