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 every trial is terminal, the next generation is produced by tournament selection + uniform crossover + Gaussian mutation, with elitism. Completed trials contribute their scores; pruned or failed members are represented by worst sentinel fitness.

Synchronous evolution (F3): the population evolves only once its whole generation is resolved (liar = none). ask_batch therefore returns a partial batch at a generation boundary — finish the current generation before asking further. Any non-none liar value is rejected at optimizer creation with N4M_ERR_NOT_IMPLEMENTED. 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 is intended for combinatorial / rugged search surfaces such as mixed categorical+numeric spaces. Conditional activation is applied after candidate decoding. The genome contains one unit coordinate per declared axis, but the coordinate for a sorted_tuple is unused; tuple components are sampled independently by the base RNG. Search spaces containing hard mutex_group, requires or exclude constraints are rejected by n4m_optimizer_create with N4M_ERR_UNSUPPORTED; GA never delegates infeasibility to a host fitness penalty. Hard constraints that reference a tuple root are rejected for every sampler.

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 target: the population trajectory is deterministic for a fixed seed, ask/tell order and score tape. Track-Q commits the native ga_sphere2 trace as a target for future bindings; it does not yet exercise GA with every pruner or search-space feature. Convergence on a continuous 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.