Crate fcmaes_core 

fcmaes-core

fcmaes-core provides fast, parallel, gradient-free optimization algorithms implemented entirely in Rust. It is the reusable optimizer core of the fcmaes-rust project and does not compile, link, load, or call the historical C++ optimizer backend.

Installation

cargo add fcmaes-core

The minimum supported Rust version is 1.88.

Minimal example

use fcmaes_core::{De, DeParams, Fitness};

fn sphere(x: &[f64]) -> f64 {
    x.iter().map(|value| value * value).sum()
}

fn main() {
    let dim = 5;
    let lower = vec![-5.0; dim];
    let upper = vec![5.0; dim];
    let fitness = Fitness::bounded(dim, 1, &lower, &upper);
    let params = DeParams {
        max_evaluations: 20_000,
        seed: 1,
        ..Default::default()
    };
    let mut optimizer = De::new(fitness, &[], &[], None, &params);
    let result = optimizer.optimize(&sphere);

    println!(
        "value={} evaluations={} x={:?}",
        result.y, result.evaluations, result.x
    );
}

Use optimized builds for real workloads:

cargo run --release

Capabilities

• Differential Evolution, active CMA-ES, CR-FM-NES, PGPE and Dual Annealing
• BiteOpt and batched ask/tell optimization
• MODE multi-objective optimization
• independent, coordinated and weighted multi-objective retry
• CVT MAP-Elites, quality-diversity search and the Diversifier
• native multithreading and parallel batch evaluation

API map

Problem	Start here
General bounded scalar optimization	De, Cmaes, or BiteOpt
High-dimensional or noisy search	Pgpe or Crfmnes
Independent or adaptive restarts	retry
Several competing objectives	mode and moretry
Diverse high-quality solutions	mapelites
External, GPU, service, or custom batch evaluator	the ask/tell methods on DE, CMA-ES, CR-FM-NES, PGPE, BiteOpt, and MODE

Every public item is documented. Module pages provide runnable examples, algorithm references, parameter semantics, stopping behavior, and execution notes. The crate rejects undocumented public additions and broken rustdoc links at compile time.

Documentation

• API reference
• Rendered user guide
• Getting started
• Choosing an optimizer
• Optimizer guide
• Retry guide

Avoid CPU oversubscription when combining retry-level and population-evaluation parallelism. Debug builds are not representative of optimizer performance.

License

MIT

Re-exports

pub use biteopt::BiteOpt;

pub use biteopt::BiteParams;

pub use biteopt::BiteResult;

pub use biteopt::DeepBiteOpt;

pub use biteopt::optimize_bite;

pub use biteopt::validate_bite_inputs;

pub use cmaes::AcmaResult;

pub use cmaes::Cmaes;

pub use cmaes::CmaesParams;

pub use crfmnes::Crfmnes;

pub use crfmnes::CrfmnesParams;

pub use crfmnes::CrfmnesResult;

pub use da::DaParams;

pub use da::DaResult;

pub use da::optimize_da;

pub use de::De;

pub use de::DeParams;

pub use de::DeResult;

pub use fitness::Fitness;

pub use fitness::NAN_REPLACEMENT;

pub use fitness::Objective;

pub use fitness::parallel_batch;

pub use mapelites::Archive;

pub use mapelites::DiversifierParams;

pub use mapelites::MapElitesParams;

pub use mapelites::QdBatchFitness;

pub use mapelites::QdFitness;

pub use mapelites::diversify;

pub use mapelites::diversify_batch;

pub use mapelites::map_elites;

pub use mapelites::map_elites_batch;

pub use mapelites::map_elites_batch_with_progress;

pub use mode::Mode;

pub use mode::ModeParams;

pub use mode::ModeResult;

pub use moretry::MoRetryConfig;

pub use moretry::MoRetryEntry;

pub use moretry::MoRetryResult;

pub use moretry::MultiObjective;

pub use moretry::WeightedObjective;

pub use moretry::moretry;

pub use moretry::pareto_indices;

pub use moretry::scalarize;

pub use pgpe::Pgpe;

pub use pgpe::PgpeParams;

pub use pgpe::PgpeResult;

pub use retry::AdvancedRetryConfig;

pub use retry::RetryBounds;

pub use retry::RetryConfig;

pub use retry::RetryContext;

pub use retry::RetryEntry;

pub use retry::RetryImprovement;

pub use retry::RetryResult;

pub use retry::RetryRunResult;

pub use retry::advanced_retry;

pub use retry::retry;

pub use rng::Rng;

Modules

biteopt

BiteOpt, Aleksey Vaneev’s adaptive derivative-free optimizer.

cmaes

Active Covariance Matrix Adaptation Evolution Strategy (CMA-ES).

crfmnes

CR-FM-NES for high-dimensional derivative-free optimization.

da

Dual Annealing with optional bounded local search.

de

Differential Evolution (DE) for bounded, derivative-free optimization.

fitness

Bounds handling, coordinate normalization, and parallel evaluation.

mapelites

Quality-Diversity search with CVT-MAP-Elites and the Diversifier.

mode

Multi-objective Differential Evolution (MODE).

moretry

Parallel weighted-scalarization retry for multi-objective problems.

pgpe

Parameter-Exploring Policy Gradients (PGPE).

retry

Parallel optimization restart coordinators.

rng

Random number generation for the optimizers.

Constants

CORE_VERSION

Version string of the core crate, surfaced through the Python build-info.

Functions

probe_sum

Sum a slice of f64.