ga — genetic algorithm (sampler)

Role: optimization · kind: n4m_sampler_kind_t = N4M_SAMPLER_GA · since: ABI 2.1 (F3)

Real-coded genetic algorithm over the unit hypercube. Every candidate is a unit vector u ∈ [0,1)^P decoded per parameter (numeric_from_unit for numeric axes; bucketed for categorical / ordinal), so mixed continuous / discrete / categorical spaces are handled uniformly. A generation of pop_size (= 16 in F3) candidates is handed out via ask(); once those trials report scores, the next generation is produced by tournament selection + uniform crossover + Gaussian mutation, with elitism (the best candidate is carried over unchanged, so the incumbent never regresses).

Synchronous evolution (F3): the population evolves only once its whole generation is scored (liar = none). ask_batch therefore returns a partial batch at a generation boundary — score the current generation before asking further. Warm-start (n4m_optimizer_enqueue) is not supported for population samplers (a forced candidate cannot be inverse-encoded into the genome) and returns N4M_ERR_UNSUPPORTED.

GA suits combinatorial / rugged search surfaces — preprocessing-chain choices, mixed categorical+numeric spaces — where gradient-free population search beats independent sampling. Sorted-tuple axes are sampled by the base sampler (not encoded in the genome). Hard constraints are handled through fitness (the host scores an infeasible candidate poorly), not by rejection.

Note: this is the HPO-sampler GA over the typed search space — distinct from the feature-selection n4m_feature_selection_ga_select (a GA over feature masks). A later F3 refinement may share the RNG-consolidated population loops between them (see FINETUNING_ROADMAP.md); for now they are independent.

Usage (C ABI)

n4m_optimizer_options_t opts;
n4m_optimizer_options_init(&opts);
opts.sampler = N4M_SAMPLER_GA;
opts.seed = 42;
/* standard ask/tell loop; run enough trials for several generations */

Parity

  • Tier B-state: the population trajectory is a deterministic function of the seed + the reported fitnesses; identical across bindings at a fixed seed via the shared n4m_rng. Convergence on a continuous test objective is verified in the C++ tests.

References

  • Leardi & Lupiáñez González, Genetic algorithms applied to feature selection in PLS regression, Chemom. Intell. Lab. Syst. 41 (1998), 195–207. See _finetuning_bibliography.bib.