rlevo_evolution/algorithms/mod.rs
1//! Concrete evolutionary algorithms.
2//!
3//! # Classical families
4//!
5//! - [`ga`] / [`ga_binary`] — Genetic Algorithm (real- and binary-coded).
6//! - [`es_classical`] — (1+1), (1+λ), (μ,λ), (μ+λ) Evolution Strategies.
7//! - [`cma_es`] — Covariance Matrix Adaptation ES (CSA + evolution paths +
8//! rank-1/rank-μ covariance updates).
9//! - [`cmsa_es`] — Covariance Matrix Self-Adaptation ES (path-free; per-individual
10//! log-normal σ + rank-μ ML covariance blend).
11//! - [`de`] — Differential Evolution (rand/best/current-to-best × bin/exp).
12//! - [`ep`] — Evolutionary Programming (Fogel-style).
13//! - [`gp_cgp`] — Cartesian Genetic Programming.
14//! - [`gep`] — Gene Expression Programming (linear head/tail genome decoded to
15//! an expression tree).
16//!
17//! # Swarm / nature-inspired metaheuristics
18//!
19//! - [`metaheuristic`] — PSO, `ACO_R`, ABC, GWO, WOA, CS, FA, BA, SSA.
20//!
21//! # Estimation-of-distribution algorithms
22//!
23//! - [`eda`] — [`EdaStrategy`](eda::EdaStrategy) over a
24//! [`ProbabilityModel`](crate::ProbabilityModel): UMDA, PBIL, compact-GA,
25//! and a dependency-chain MIMIC model.
26//!
27//! # Hybrid / composite strategies
28//!
29//! - [`memetic`] — [`MemeticWrapper`](memetic::MemeticWrapper): wraps any
30//! real-valued strategy with per-individual local-search refinement
31//! (Lamarckian / Baldwinian / Partial writeback).
32//! - [`neuroevolution`] — [`WeightOnly`](neuroevolution::WeightOnly): wraps any
33//! real-valued strategy to evolve the flattened weights of a Burn `Module`.
34
35pub mod cma_es;
36pub mod cmsa_es;
37pub mod de;
38pub mod eda;
39pub mod ep;
40pub mod es_classical;
41pub mod ga;
42pub mod ga_binary;
43pub mod gep;
44pub mod gp_cgp;
45pub mod memetic;
46pub mod metaheuristic;
47pub mod neuroevolution;