Extract shared evolutionary algorithm infrastructure (genetic operators,
candidate management, Das-Dennis reference points) from NSGA-II into a
new genetic.rs module, then build two new multi-objective samplers on top:
- NSGA-III: reference-point-based niching for well-distributed fronts
on 3+ objective problems (Das-Dennis structured points, normalization,
perpendicular distance association, niching selection)
- MOEA/D: decomposition-based optimization with three scalarization
methods (Tchebycheff, WeightedSum, PBI), weight-vector neighborhoods,
and neighborhood-based mating selection
Both implement MultiObjectiveSampler with builder pattern, seeded RNG,
and SBX crossover / polynomial mutation via the shared genetic module.
Population-based optimizer with mutation + crossover. Supports three
strategies (Rand1, Best1, CurrentToBest1), configurable F and CR,
and auto-sized populations. No extra dependencies needed.
fastrand is smaller, faster, and has no dependencies. Add rng_util
helper for f64 range generation since fastrand lacks a built-in
equivalent. Migrate all samplers, KDE modules, and fANOVA to use
fastrand's concrete Rng type instead of rand's trait-based generics.
Implement a classical Bayesian optimization sampler using a GP surrogate
with Matérn 5/2 kernel and Expected Improvement acquisition function.
Feature-gated behind `gp = ["dep:nalgebra"]`.
Extend TPE to handle multi-objective optimization using Pareto-based
splitting. MOTPE uses non-dominated sorting to define "good" (front 0)
vs "bad" (dominated) regions for the KDE models, replacing the
single-objective gamma-based split.
Add MultiObjectiveStudy for optimizing multiple objectives simultaneously,
backed by NSGA-II (Non-dominated Sorting Genetic Algorithm II) with SBX
crossover, polynomial mutation, and constraint-aware dominance.
New public API:
- MultiObjectiveStudy with optimize(), pareto_front(), ask()/tell()
- MultiObjectiveTrial with get(), is_feasible(), user attributes
- MultiObjectiveSampler trait for custom MO samplers
- Nsga2Sampler with builder for population size, crossover/mutation params
- ObjectiveDimensionMismatch error variant
Add constraint support so that optimization problems with constraints
(e.g., "model size < 100MB") prefer feasible solutions. Convention:
constraint value <= 0.0 means feasible.
- Add constraint_values field to Trial with set_constraints/getter
- Add constraints field to CompletedTrial with is_feasible() method
- Propagate constraints through complete_trial/prune_trial
- Make best_trial() and top_trials() constraint-aware: feasible trials
rank above infeasible, infeasible ranked by total violation
BOHB combines TPE's model-guided sampling with Hyperband's budget-aware
evaluation by conditioning the TPE model on trials at specific budget
levels, producing better-calibrated parameter proposals.
Implement CMA-ES (Covariance Matrix Adaptation Evolution Strategy) as a
new sampler for continuous optimization. Uses nalgebra for matrix
operations and implements the full Hansen 2016 algorithm: mean update,
evolution paths, covariance matrix adaptation, and cumulative step-size
adaptation.
Key features:
- Builder API with configurable sigma0, population_size, and seed
- Three-phase state machine: discovery, active sampling, generation update
- Rejection sampling with bounds clipping fallback
- Log-scale and step parameter support via internal-space mapping
- Categorical parameters handled via random sampling fallback
- Numerical robustness: eigenvalue clamping, symmetry enforcement
Add SobolSampler using Owen-scrambled Sobol sequences (sobol_burley
crate) for better uniform coverage of the parameter space compared to
random sampling. Supports all distribution types including log-scale
and stepped parameters.
Add Serialize/Deserialize derives to all public data types (ParamValue,
Distribution, Direction, TrialState, ParamId, AttrValue, CompletedTrial)
gated behind a `serde` feature flag. Introduce StudySnapshot struct and
Study::save()/Study::load() for persisting and restoring study state as
human-readable JSON.
Add AttrValue enum (Float, Int, String, Bool) with From impls and
set_user_attr/user_attr/user_attrs methods on both Trial and
CompletedTrial. Attributes set during optimization are propagated
through complete_trial and prune_trial. AttrValue is re-exported
at crate root and in the prelude.
- Add `Pruned` variant to `TrialState`
- Add `Error::TrialPruned` variant and standalone `TrialPruned` struct
with `From<TrialPruned> for Error` for ergonomic `?` usage
- Add `state` field to `CompletedTrial` (defaults to `Complete`)
- Add `Study::prune_trial()` and `Study::n_pruned_trials()`
- `optimize()` and `optimize_with_callback()` detect `TrialPruned`
errors via Any downcasting and record pruned trials instead of
failing them
- `best_trial()` / `best_value()` now filter to only `Complete` trials
- Re-export `TrialPruned` from crate root and prelude
- Add report(step, value) and should_prune() methods to Trial
- Store intermediate_values in Trial, copied to CompletedTrial on completion
- Pass pruner through trial factory so trials can consult it
- Rebuild trial factory when pruner is changed via set_pruner()
- Add `.name()` builder method on all 5 parameter types for custom labels
- Add `CompletedTrial::get(¶m)` for typed parameter access
- Add `Display` impl on `ParamValue`
- Add prelude module at `optimizer::prelude::*`
- Shadow `_with_sampler` methods on `Study<f64>` so `optimize()` auto-uses
the configured sampler; deprecate `_with_sampler` variants
- Use runtime `Any` downcasting with `trial_factory` to avoid E0592
- Update all examples and tests to use the new API
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Manuel Raimann