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feat: add Multi-Objective TPE (MOTPE) sampler

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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.
This commit is contained in:
Manuel Raimann
2026-02-11 19:43:01 +01:00
parent bcc4549e66
commit ba31df69c7
3 changed files with 936 additions and 0 deletions
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src/lib.rs
+3
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@@ -21,6 +21,7 @@
//! - **CMA-ES** - Covariance Matrix Adaptation Evolution Strategy for continuous optimization (requires `cma-es` feature)
//! - **BOHB** - Bayesian Optimization + `HyperBand` for budget-aware TPE sampling
//! - **NSGA-II** - Non-dominated Sorting Genetic Algorithm II for multi-objective optimization
//! - **MOTPE** - Multi-Objective Tree-Parzen Estimator for Bayesian multi-objective optimization
//!
//! Additional features include:
//!
@@ -246,6 +247,7 @@ pub use sampler::bohb::BohbSampler;
#[cfg(feature = "cma-es")]
pub use sampler::cma_es::CmaEsSampler;
pub use sampler::grid::GridSearchSampler;
pub use sampler::motpe::MotpeSampler;
pub use sampler::nsga2::Nsga2Sampler;
pub use sampler::random::RandomSampler;
#[cfg(feature = "sobol")]
@@ -281,6 +283,7 @@ pub mod prelude {
#[cfg(feature = "cma-es")]
pub use crate::sampler::cma_es::CmaEsSampler;
pub use crate::sampler::grid::GridSearchSampler;
pub use crate::sampler::motpe::MotpeSampler;
pub use crate::sampler::nsga2::Nsga2Sampler;
pub use crate::sampler::random::RandomSampler;
#[cfg(feature = "sobol")]