| SalpSwarm |
Table-driven preset-backed algorithm wrapper classes. |
| sezgi_version |
Version of the underlying sezgi Rust core. |
| SHADE |
Table-driven preset-backed algorithm wrapper classes. |
| SimulatedAnnealing |
Table-driven preset-backed algorithm wrapper classes. |
| SineCosineAlgorithm |
Table-driven preset-backed algorithm wrapper classes. |
| SzRng |
An owned per-call RNG handle exposed to an R-authored 'generate'/ 'initialize' callback as the 'rng' argument - an external-pointer- backed '#[savvy]' object ('SzRng$new'/'SzRng$from_master'/method closures are all auto-generated by 'savvy-cli', the SAME mechanism 'EvalSession' already uses, 'session.rs''s own module doc). Wraps a CLONE of the stage's live 'RngStream' - mirrors 'PyRng' ('py-sezgi/ src/lib.rs') exactly: every method here delegates 1:1 to 'RngStream''s own 'pub' API ('crates/core/src/rng.rs:16-59'), no new RNG logic. |
| sz_algorithm |
Declares a pure-R metaheuristic algorithm to run over an 'EvalSession'. |
| sz_algo_solve |
Drives an 'sz_algorithm' over an 'EvalSession' to exhaustion. |
| sz_as_problem |
Accepts a 'Problem' subclass instance. |
| sz_bayesian_plackett_luce |
Bayesian Plackett-Luce posterior via Gibbs sampling. |
| sz_bias_central |
Central-bias scan: run an algorithm spec on paired centered/shifted BBOB conditions and test whether its performance gap differs between them (Kudela's center-bias-exploitation method). See 'crates/bias/src/central.rs' for the full method provenance. |
| sz_bias_report |
One-call bias report: runs both the structural and central bias scans on one algorithm spec and assembles a single report with a ready-to-paste LaTeX summary table. See 'crates/bias/src/report.rs' for the full assembly/LaTeX-rendering details. |
| sz_bias_structural |
Structural-bias scan: run an algorithm spec repeatedly on the f0 random-function null problem and test its final positions for departure from uniformity (BIAS-toolbox method; Kononova et al. 2015 / Vermetten et al. 2022). See 'crates/bias/src/structural.rs' for the full method provenance. |
| sz_bias_structural_positions |
Statistics-only structural-bias scan over externally-collected final positions - the bias bridge for algorithms authored OUTSIDE this package's own spec/engine (M3-5 Task 6), e.g. a pure-R 'sz_algorithm' driven by 'sz_algo_solve' over 'sz_eval_session_f0', one run at a time. Runs the SAME KS/AD/ Holm battery as 'sz_bias_structural' over caller-supplied 'final_positions' instead of driving an algorithm spec through the engine itself - see 'crates/bias/src/structural.rs"s 'scan_from_positions' for the full method provenance. Mirrors py-sezgi's 'sezgi.bias.structural_positions()' 1:1. |
| sz_binary |
A binary block: 'n' bits. |
| sz_builtin_bbob |
A built-in-problem descriptor for [Algorithm]'s 'run()'. |
| sz_categorical |
A categorical block: 'n' genes, each a category index in '0..k'. |
| sz_cec2014_evaluate |
Direct, one-shot evaluation of a CEC 2014 (Liang, Qu & Suganthan 2013) function at 'x', bypassing 'sz_solve_bbob'-style budget/engine machinery entirely - binds ['Cec2014::new'] + ['Cec2014::evaluate_batch'] exactly. Mirrors 'sz_cec2022_evaluate' (M3-6 Task 10 - see py-sezgi's 'sezgi.problems.cec2014_evaluate' for the identical binding on the Python side). See ['Cec2014::new']'s own doc for the exact 'fid'/'dim' domain. |
| sz_cec2014_f_star |
The report's pinned 'F_i*' bias for a CEC 2014 function - binds ['Cec2014::f_star'] ('F_i* = 100*fid'). Does not depend on 'dim', so an internal probe 'dim = 10' is used purely to validate 'fid'. |
| sz_cec2017_evaluate |
Direct, one-shot evaluation of a CEC 2017 (Awad, Ali, Liang, Qu & Suganthan 2016) function at 'x', bypassing 'sz_solve_bbob'-style budget/engine machinery entirely - binds ['Cec2017::new'] + ['Cec2017::evaluate_batch'] exactly. Mirrors 'sz_cec2014_evaluate' (M3-6 Task 10 - see py-sezgi's 'sezgi.problems.cec2017_evaluate' for the identical binding on the Python side). See ['Cec2017::new']'s own doc for the exact 'fid'/'dim' domain. |
| sz_cec2017_f_star |
The report's pinned 'F_i*' bias for a CEC 2017 function - binds ['Cec2017::f_star'] ('F_i* = 100*fid', the fid-gapped C dispatch bias, not the report's contiguous renumbering - see 'Cec2017::new''s own module doc). Does not depend on 'dim', so an internal probe 'dim = 10' is used purely to validate 'fid'. |
| sz_cec2022_evaluate |
Direct, one-shot evaluation of a CEC 2022 function at 'x', bypassing 'sz_solve_bbob'-style budget/engine machinery entirely - binds ['Cec2022::new'] + ['Cec2022::evaluate_batch'] exactly. See 'Cec2022::new''s own doc for the exact 'fid'/'dim' domain. |
| sz_cec2022_f_star |
The report's pinned 'F_i*' bias for a CEC 2022 function - binds ['Cec2022::f_star'] (module doc section 1.2's table). 'f_star' does not depend on 'dim', so an internal probe 'dim = 10' is used purely to validate 'fid' (every 'fid' in '1..=12' accepts 'dim = 10', hybrids included). |
| sz_coco_export |
Exports the IOH archive at 'log_root' as a COCO/BBOB "old format" archive rooted at 'out_dir' - see 'sezgi_bench::coco_export'. Returns the list of written file paths (as strings), sorted for determinism. |
| sz_ecdf |
ECDF/anytime curve(s) over an on-disk IOH archive. |
| sz_eval_session |
Start an ask/tell evaluation session over a BBOB problem. |
| sz_eval_session_cec2014 |
Start an ask/tell evaluation session over a CEC 2014 problem. |
| sz_eval_session_cec2017 |
Start an ask/tell evaluation session over a CEC 2017 problem. |
| sz_eval_session_cec2022 |
Start an ask/tell evaluation session over a CEC 2022 problem. |
| sz_eval_session_f0 |
Start an ask/tell evaluation session over the f0 BIAS-toolbox null problem. |
| sz_eval_session_tsp |
Start an ask/tell evaluation session over a vendored TSPLIB (TSP) instance. |
| sz_float |
A continuous block: 'n' coordinates, each in '[lo, hi]'. |
| sz_int |
An integer block: 'n' coordinates, each in '[lo, hi]' (inclusive). |
| sz_mo_evaluate |
Direct, one-shot objective evaluation of a decision vector 'x' against any 'sz_mo_*' problem string, bypassing 'sz_nsga2()''s population/budget machinery entirely. Added (M3-7) so fixture-value tests can pin an exact 'x' ('sz_nsga2()''s randomly-initialized population cannot), mirroring the existing 'sz_cec2022_evaluate()'/'sz_cec2014_evaluate()'/ 'sz_cec2017_evaluate()' one-shot-evaluation convention. |
| sz_mo_evaluate_constraints |
The matching one-shot constraint-row evaluation for 'sz_mo_evaluate()', same calling convention. |
| sz_mo_hypervolume |
General-M exact hypervolume (While, Bradstreet & Barone 2012, the WFG algorithm; 'M == 2' delegates internally to the SAME ['sezgi_stats::hypervolume_2d']) - binds ['sezgi_stats::hypervolume'] (M3-7 Task 8/11). Unlike 'sz_mo_hypervolume_2d', 'front'/'ref_point' may have any number 'M >= 1' of objectives. |
| sz_mo_hypervolume_2d |
Exact 2-objective hypervolume (Zitzler & Thiele 1999 S-metric, reference-point variant; minimization) - binds ['sezgi_stats::hypervolume_2d'] exactly. See that function's doc for the pinned definition. |
| sz_mo_igd |
Inverted Generational Distance (Ishibuchi et al. 2015, eq. 12, 'p = 1') - binds ['sezgi_stats::igd'] exactly. Any (equal, consistent) number of objectives across both 'front' and 'reference_front'. |
| sz_mo_pareto_front |
A deterministic 'n'-point sample of the analytic Pareto front in OBJECTIVE space, if known. See 'MoProblem::pareto_front' in 'crates/core/src/mo.rs'. |
| sz_mo_read_moa |
Reads a "sezgi-moa v1" archive file written by 'sz_nsga2(..., log_dir =, label =)' (M3-7 Task 9/11) - binds ['sezgi_bench::read_moa']. Returns a named list: - 'algo' (character): the logging algorithm name - always '"nsga2"' today ('nsga2_run_logged''s own fixed 'NSGA2_ALGO_NAME'). - 'problem' (character): the 'label' 'sz_nsga2' was called with. **Kept as the literal on-disk header key name** ('crates/bench/src/mo_archive.rs''s own format grammar: the header line is 'problem <label>', not 'label <label>') rather than renamed here to '"label"' - this binding stays a thin, direct mirror of 'MoArchiveRun''s own field names, so a reader cross-checking against the Rust struct (or the Python binding, M3-7 Task 10) sees the SAME key everywhere. - 'm', 'seed', 'budget' (double, whole-number-valued - R has no native integer64). - 'kind' (character): '"float"' or '"binary"'. - 'records' (list, in file/eval order): each entry a named list with 'eval_index' (double), 'objectives' (numeric vector), 'genotype' (numeric vector for 'kind = "float"', 'logical' vector for 'kind = "binary"' - see this module's own doc, "Container-idiom decisions"). - 'archive' (list of numeric vectors): the reconstructed nondominated archive at evaluation budget 'at', via 'MoArchiveRun::archive_at'. |
| sz_nsga2 |
NSGA-II (Deb, Pratap, Agarwal & Meyarivan 2002) run on a ZDT/DTLZ/WFG multi-objective test problem. See 'crates/components/src/nsga2.rs' for the full algorithm provenance. |
| sz_permutation |
A permutation block: one permutation of '0..n'. |
| sz_per_budget_packages |
Build one paper-package statistics list PER DISTINCT BUDGET present in a 'sz_run_experiment()' data.frame, in ascending budget order. |
| sz_preset_abc |
Builds an Artificial Bee Colony spec (Karaboga 2005, TR-06 / Karaboga & Basturk 2007, Journal of Global Optimization - a labeled metaphor preset; see 'crates/components/src/abc.rs''s module doc for the full provenance extraction against the author's own 'ABCorig.m' plus the official 'Python_ABC' port, the pop<->food-source convention resolution, the fitness-transform monotonicity proof, and the phase-design adjudication - a SINGLE stage, not two symmetric stages like 'tlbo': 'gen/abc-employed' + the new 'replace/abc-trial-greedy', plus the new 'adapter/abc-onlooker-scout' folding the onlooker AND scout phases together) as JSON, ready to pass to 'sz_solve_bbob()'. |
| sz_preset_alo |
Builds an Ant Lion Optimizer spec (Mirjalili 2015, Advances in Engineering Software - a labeled metaphor preset; see 'crates/components/src/alo.rs''s module doc for the full provenance extraction against the author's own 'ALO.m'/'Random_walk_around_ antlion.m'/'RouletteWheelSelection.m', the faithful-full-walk cost decision, and the elitism design adjudication - the antlion population itself is the persisted memory via 'replace/mu-plus-lambda', no blackboard state needed) as JSON, ready to pass to 'sz_solve_bbob()'. |
| sz_preset_bat |
Builds a Bat Algorithm spec (Yang, X.-S. 2010, NICSO - a labeled metaphor preset, see 'crates/components/src/ba.rs''s module doc for the tier note, citation, the verified 'bat_algorithm.m' loop structure, the verified fixed-loudness/pulse-rate finding, the two composing sign-inversion deltas in the frequency draw and velocity term, and the design adjudication for the new 'replace/bat-loudness-greedy' acceptance-coupled replacer) as JSON, ready to pass to 'sz_solve_bbob()'. |
| sz_preset_classes |
Table-driven preset-backed algorithm wrapper classes. |
| sz_preset_cmaes |
Builds a (mu/mu_w,lambda)-CMA-ES algorithm spec as JSON, ready to pass to 'sz_solve_bbob()'. |
| sz_preset_cmaes_ipop |
Builds a CMA-ES with IPOP-style stagnation restarts algorithm spec as JSON, ready to pass to 'sz_solve_bbob()'. |
| sz_preset_cuckoo_search |
Builds a Cuckoo Search algorithm spec (Yang & Deb 2009 - a labeled metaphor preset, see 'crates/components/src/cs.rs''s module doc for the tier note, citation and pinned draw order) as JSON, ready to pass to 'sz_solve_bbob()'. |
| sz_preset_de_best_1 |
Builds a DE/best/1/bin algorithm spec (uniform init, clamp boundary, one-to-one-greedy replacement) as JSON, ready to pass to 'sz_solve_bbob()'. |
| sz_preset_de_rand_1 |
Builds a DE/rand/1/bin algorithm spec (uniform init, clamp boundary, one-to-one-greedy replacement) as JSON, ready to pass to 'sz_solve_bbob()'. |
| sz_preset_es_mu_plus_lambda |
Builds a (mu/mu_w,lambda)-ES algorithm spec (mutation step drawn from 'dist') as JSON, ready to pass to 'sz_solve_bbob()'. |
| sz_preset_firefly |
Builds a Firefly Algorithm (Yang, X.-S., *Nature-Inspired Metaheuristic Algorithms*, 2nd ed., Luniver Press, 2010 - a labeled metaphor preset, see 'crates/components/src/fa.rs''s module doc for the tier note, citation, the verified 'fa_ndim.m'/'ffa_move.m' loop structure, the floored attractiveness formula, the closed-form 'alpha' decay, and the hybrid in-place-self/live-distance/frozen-target double-loop semantics) spec as JSON, ready to pass to 'sz_solve_bbob()'. |
| sz_preset_fpa |
Builds a Flower Pollination Algorithm spec (Yang, X.-S. 2012, UCNC - a labeled metaphor preset, see 'crates/components/src/fpa.rs''s module doc for the tier note, citation, the verified 'fpa_demo.m' loop structure, the switch-branch orientation delta, the global-step sign delta reusing 'cs.rs''s 'cs_dim_step' verbatim, the local-step self-selection-not- excluded finding, and the min_pop adjustment from 3 down to 2) as JSON, ready to pass to 'sz_solve_bbob()'. |
| sz_preset_ga_bin |
Builds a Binary-space GA spec (tournament selection, uniform crossover, bit-flip mutation - 'gen/ga-bin' + 'replace/mu-plus-lambda') as JSON, ready to pass to 'sz_solve_onemax()'. Binds ['sezgi_components::presets::ga_bin'] exactly - M3-8 Task 10, mirroring py-sezgi's 'sezgi.presets.ga_bin' (M3-8 Task 9). |
| sz_preset_ga_cat |
Builds a Categorical-space GA spec (tournament selection, uniform crossover, random-reset mutation - 'gen/ga-cat' + 'replace/mu-plus-lambda') as JSON, ready to pass to 'sz_solve_cat_match()'. Binds ['sezgi_components::presets::ga_cat'] exactly - M3-8 Task 10, mirroring py-sezgi's 'sezgi.presets.ga_cat' (M3-8 Task 9). |
| sz_preset_ga_int |
Builds an Int-space GA spec (tournament selection, SBX-style integer crossover, polynomial-style integer mutation - 'gen/ga-int' + 'replace/mu-plus-lambda') as JSON, ready to pass to 'sz_solve_int_quadratic()'. Binds ['sezgi_components::presets::ga_int'] exactly - M3-8 Task 10, mirroring py-sezgi's 'sezgi.presets.ga_int' (M3-8 Task 9). |
| sz_preset_ga_perm |
Builds a permutation-space GA spec (tournament selection, order crossover, swap mutation - 'gen/ga-perm' + 'replace/mu-plus-lambda') as JSON, ready to pass to 'sz_solve_tsp()'. Binds ['sezgi_components::presets::ga_perm'] exactly - same preset py-sezgi's 'sezgi.presets.ga_perm' binds (M3-3 Task 9). |
| sz_preset_ga_real |
Builds a real-coded GA (SBX crossover, polynomial mutation) algorithm spec as JSON, ready to pass to 'sz_solve_bbob()'. |
| sz_preset_goa |
Builds a Grasshopper Optimisation Algorithm spec (Saremi, Mirjalili & Lewis 2017 - a labeled metaphor preset, see 'crates/components/src/goa.rs''s module doc for the tier note, citation, the IMPLEMENTER-VERIFY distance-normalization resolution and the zero-RNG-draw arithmetic-order pin) as JSON, ready to pass to 'sz_solve_bbob()'. |
| sz_preset_gsa |
Builds a Gravitational Search Algorithm spec (Rashedi, Nezamabadi-pour & Saryazdi 2009, Information Sciences - a labeled metaphor preset, and the wave's LAST stateful/blackboard algorithm; see 'crates/components/src/gsa.rs''s module doc for the full provenance extraction against the author's own 'GSA.m'/'Gconstant.m'/ 'massCalculation.m'/'Gfield.m'/'move.m', the verified 'M_i'-free force delta, and the confirmation that GSA's own 'Fbest'/'Lbest' never feed back into the mechanism) as JSON, ready to pass to 'sz_solve_bbob()'. |
| sz_preset_gwo |
Builds a Grey Wolf Optimizer algorithm spec (Mirjalili, Mirjalili & Lewis 2014 - a labeled metaphor preset, see 'crates/components/src/gwo.rs''s module doc for the tier note and citations) as JSON, ready to pass to 'sz_solve_bbob()'. |
| sz_preset_harmony_search |
Builds a Harmony Search algorithm spec (Geem, Kim & Loganathan 2001 - a labeled metaphor preset, see 'crates/components/src/hs.rs''s module doc for the tier note and citations) as JSON, ready to pass to 'sz_solve_bbob()'. |
| sz_preset_hho |
Builds a Harris Hawks Optimization spec (Heidari, Mirjalili, Faris, Aljarah, Mafarja & Chen 2019, Future Generation Computer Systems - a labeled metaphor preset, and the wave's most structurally complex one: a multi-branch escape-energy tree whose progressive rapid-dive sub-branches evaluate mid-'generate()'. See 'crates/components/src/hho.rs''s module doc for the full provenance extraction against the paper author's own 'HHO.m', the hard/soft besiege mapping delta, the mean(X)/random-hawk in-place semantics, and the prominent in-generator-evaluation eval-accounting design decision) as JSON, ready to pass to 'sz_solve_bbob()'. |
| sz_preset_jaya |
Builds a JAYA spec (Rao 2016 - a labeled metaphor preset, see 'crates/components/src/jaya.rs''s module doc for the tier note, citation, the primary-paper-verified worked-example reproduction, the shared-per-dimension-per-generation 'r1'/'r2' draw finding and the greedy-replacement delta vs mealpy's misleadingly-named 'OriginalJA') as JSON, ready to pass to 'sz_solve_bbob()'. |
| sz_preset_jde |
Builds a jDE algorithm spec (self-adaptive F/CR DE) as JSON, ready to pass to 'sz_solve_bbob()'. |
| sz_preset_lshade |
Builds an L-SHADE algorithm spec (population linearly reduced from '18 * dim') as JSON, ready to pass to 'sz_solve_bbob()'. |
| sz_preset_mfo |
Builds an MFO (Moth-Flame Optimization; Mirjalili 2015 - a labeled metaphor preset, see 'crates/components/src/mfo.rs''s module doc for the tier note, citation, the verified 'MFO.m' loop structure, the two subtle draw/index deltas found vs the plan's sketch, and the blackboard flame-memory design) spec as JSON, ready to pass to 'sz_solve_bbob()'. |
| sz_preset_nelder_mead |
Builds a Nelder-Mead simplex algorithm spec as JSON, ready to pass to 'sz_solve_bbob()'. |
| sz_preset_pso |
Builds a PSO (Clerc-Kennedy constriction) algorithm spec as JSON, ready to pass to 'sz_solve_bbob()'. |
| sz_preset_random_search |
Builds a random search algorithm spec as JSON, ready to pass to 'sz_solve_bbob()'. |
| sz_preset_sa |
Builds a simulated annealing (Metropolis, geometric cooling) algorithm spec as JSON, ready to pass to 'sz_solve_bbob()'. |
| sz_preset_sca |
Builds a Sine Cosine Algorithm spec (Mirjalili 2016 - a labeled metaphor preset, see 'crates/components/src/sca.rs''s module doc for the tier note, citation, the 'SCA.m'-verified pinned draw order and the mealpy-'OriginalSCA' replacer delta) as JSON, ready to pass to 'sz_solve_bbob()'. |
| sz_preset_shade |
Builds a SHADE algorithm spec as JSON, ready to pass to 'sz_solve_bbob()'. |
| sz_preset_ssa |
Builds an SSA (Salp Swarm Algorithm; Mirjalili et al. 2017 - a labeled metaphor preset, see 'crates/components/src/ssa.rs''s module doc for the tier note, citation, the verified 'SSA.m' half-population leader/follower split, the leader sign-branch pin, the verified in-place follower-chain semantics, and the persisted-food-vs-current-pop-best delta) spec as JSON, ready to pass to 'sz_solve_bbob()'. |
| sz_preset_tlbo |
Builds a Teaching-Learning-Based Optimization spec (Rao, Savsani & Vakharia 2011, Computer-Aided Design - a labeled metaphor preset, and sezgi's FIRST multi-stage preset: two '[[stages]]' (teacher, then learner) run in sequence every generation. See 'crates/components/src/tlbo.rs''s module doc for the full provenance extraction against Yarpiz's 'tlbo.m' - explicitly labeled third-party, not Rao's own code - the per-learner teaching-factor finding, the unconditionally-distinct partner-selection finding, the min_pop adjustment from 3 down to 2, and the "parameter-free" framing's Črepinšek/Liu/Mernik (2012) counterpoint) as JSON, ready to pass to 'sz_solve_bbob()'. |
| sz_preset_woa |
Builds a Whale Optimization Algorithm spec (Mirjalili & Lewis 2016 - a labeled metaphor preset, see 'crates/components/src/woa.rs''s module doc for the tier note and citations) as JSON, ready to pass to 'sz_solve_bbob()'. |
| sz_read_ioh_records |
Reconstructs 'RunRecord's from an on-disk IOH archive at 'log_root' (as written by 'sz_run_experiment(..., log_dir = ...)'), one record per '(run, budget)' pair - see 'sezgi_bench::ioh_records''s doc comment for the exact 'best_f'/'evals_used' semantics and the curtailed-view-vs- independent-run distinction for budgets smaller than a run's logged budget. |
| sz_results_matrix |
Build a 'sezgi_stats'-shaped results matrix for one 'budget' from a 'sz_run_experiment()' data.frame. |
| sz_run_experiment |
Run a sezgi experiment spec across algorithms x problems x instances x seeds x budgets. |
| sz_solve_bbob |
Runs an algorithm spec on a BBOB problem and returns the result. |
| sz_solve_cat_match |
Runs an algorithm spec on ['CatMatch'] (a Categorical-block Hamming- distance-to-target matching problem; 'sezgi_problems::diagnostics::CatMatch') and returns the result - M3-8 Task 10, mirroring 'sz_solve_onemax' exactly. Pairs with 'sz_preset_ga_cat(...)'. Diagnostic only, see 'CatMatch''s own module doc. |
| sz_solve_cec2014 |
Runs an algorithm spec on a CEC 2014 (Liang, Qu & Suganthan 2013) function via ['Cec2014::new'] and returns the result - M3-6 Task 10, mirroring 'sz_solve_cec2022' exactly ('Engine::from_spec' + 'engine.run' + result conversion): same 'best_f'/'evals'/'best_x' shape. See ['Cec2014::new']'s own doc for the exact 'fid'/'dim' domain. |
| sz_solve_cec2017 |
Runs an algorithm spec on a CEC 2017 (Awad, Ali, Liang, Qu & Suganthan 2016) function via ['Cec2017::new'] and returns the result - M3-6 Task 10, mirroring 'sz_solve_cec2014' exactly. See ['Cec2017::new']'s own doc for the exact 'fid'/'dim' domain. |
| sz_solve_cec2022 |
Runs an algorithm spec on a CEC 2022 (Kumar, Price, Mohamed, Hadi & Suganthan 2021) function via ['Cec2022::new'] and returns the result - added later than the rest of the CEC surface, closing the gap where r-sezgi previously bound only direct evaluation ('sz_cec2022_evaluate'/'sz_cec2022_f_star'), with no 'solve()'-integrated path, unlike py-sezgi's 'sezgi.problems.cec2022(...)' + 'sezgi.solve()'. Mirrors 'sz_solve_bbob'/'sz_solve_tsp' exactly ('Engine::from_spec' + 'engine.run' + result conversion): same 'best_f'/'evals'/'best_x' shape, not py-sezgi's own 'solve()' dict shape ('best_f'/'best_x'/'evals_used'/ 'iterations') - the established r-sezgi 'sz_solve_*' convention governs here too. See 'Cec2022::new''s own doc for the exact 'fid'/'dim' domain. |
| sz_solve_int_quadratic |
Runs an algorithm spec on ['IntQuadratic'] (an Int-block quadratic bowl around a fixed, deterministically-derived target; 'sezgi_problems::diagnostics::IntQuadratic') and returns the result - M3-8 Task 10, mirroring 'sz_solve_onemax' exactly. Pairs with 'sz_preset_ga_int(...)'. Diagnostic only, see 'IntQuadratic''s own module doc. |
| sz_solve_mixed_diagnostic |
Runs an algorithm spec on ['MixedDiagnostic'] (this file's own Float+Int+Categorical+Binary mixed-space scaffold problem, see its own doc) and returns the result - M3-8 Task 10. Added SOLELY so 'gen/compound' (Task 5) is reachable end to end through the NORMAL R solve path, proven with a mixed-space 'AlgorithmSpec' authored as TOML (this task's own test) - UNLIKE its three siblings above (which take 'spec_json', pairing with 'sz_preset_ga_bin/ga_int/ga_cat''s own '.to_json()' presets), this function takes 'spec_toml' directly and parses it via ['AlgorithmSpec::from_toml'], the SAME entry point 'sz_run_experiment_raw''s 'ExperimentSpec::from_toml' already establishes the "hand a raw TOML document straight to the Rust core" convention for ('experiment.rs') - no R-side TOML library exists or is needed (r-sezgi has none in 'DESCRIPTION''s 'Suggests'; unlike py-sezgi's test, which parses TOML with the stdlib's own 'tomllib' into a dict before handing it to 'solve()', R has no such stdlib module, so parsing happens in Rust instead - 'AlgorithmSpec::from_toml'/'::from_json' are just two serializations of the identical schema, so this is not a private shortcut, only a different serialization entry point already used elsewhere in this same file's crate). Mirrors 'sz_solve_onemax' otherwise, EXCEPT 'run_id' is dropped (fixed to '0' internally) rather than taken as an explicit parameter - with 'spec_toml' this function already sits at 7 R-facing parameters; adding 'run_id' would push it to 8 and trip this workspace's 'clippy::too_many_arguments' gate (threshold 7, this file's ONE pre-existing exception is 'sz_preset_es_mu_plus_lambda_raw', not to be joined by a second). 'run_id = 0' matches how this scaffold is actually exercised (this task's own TOML test, mirroring py-sezgi's 'test_gen_compound_mixed_space_toml_spec_ solves_end_to_end', calls 'solve(spec, problem, master_seed=42)' with no 'run_id' override either - 'solve()''s own Python signature defaults 'run_id=0'). Test scaffolding only - NOT one of Task 5's brief-pinned diagnostics, and (unlike onemax/int_quadratic/cat_match) has no verified target: this file's own convention never surfaces 'Problem::optimum()' in a result anyway (see 'sz_solve_bbob''s own 'best_f'/'evals'/'best_x' shape), so that caveat needs no separate plumbing here. |
| sz_solve_onemax |
Runs an algorithm spec on ['OneMax'] (Goldberg 1989's classic Binary-block GA diagnostic; 'sezgi_problems::diagnostics::OneMax') and returns the result - M3-8 Task 10, mirroring 'sz_solve_tsp'/ 'sz_solve_cec2022' exactly ('Engine::from_spec' + 'engine.run'), except 'best_x' is now typed via ['genotype_to_r'] rather than assumed 'Float' (see that helper's own doc for the full type-mapping table). Pairs with 'sz_preset_ga_bin(...)'. Diagnostic only - not a benchmark, see 'OneMax''s own module doc. |
| sz_solve_tsp |
Runs an algorithm spec on a TSPLIB VENDORED instance ('"berlin52"', '"eil51"', '"st70"' - via ['Tsp::vendored']; UNLIKE 'sz_tsp_load()'/ 'sz_tsp_tour_length()' in 'problems.rs', raw TSPLIB text is not accepted here - mirrors py-sezgi's 'sezgi.problems.tsp(name)', which is likewise vendored-only) and returns the result. Same output shape as 'sz_solve_bbob()' ('best_f'/'evals'/'best_x'), not py-sezgi's own 'solve()' dict shape ('best_f'/'best_x'/'evals_used'/'iterations') - the established r-sezgi 'sz_solve_*' convention governs here, not py-sezgi's key names (see 'problems.rs''s module doc, "Index-convention decision", for the general 1-based-vs-0-based rule this function's 'best_x' also follows). |
| sz_space |
Composes one or more blocks into a search space, in the given order. |
| sz_stats_bayesian_signed_rank |
Bayesian signed-rank test with a region of practical equivalence (ROPE). |
| sz_stats_cliffs_delta |
Cliff's delta effect size for two independent (unpaired) samples. |
| sz_stats_cliffs_magnitude |
Qualitative magnitude label for a Cliff's delta value (Romano et al. 2006 thresholds). |
| sz_stats_friedman |
Runs the Friedman test on a results matrix. |
| sz_stats_paper_package |
Comprehensive statistical analysis package for algorithm comparison. |
| sz_stats_plackett_luce |
Plackett-Luce maximum-likelihood ranking (Hunter 2004 MM algorithm; see 'sezgi_stats::plackett_luce'). |
| sz_stats_wilcoxon |
Wilcoxon signed-rank test for two paired samples (Pratt zero-handling and tie correction; see 'sezgi_stats::wilcoxon_signed_rank'). |
| sz_tsp_load |
Loads a TSPLIB 'EUC_2D' instance, either a vendored instance name ('"berlin52"', '"eil51"', '"st70"') or raw TSPLIB file text (see ['load_tsp']). |
| sz_tsp_tour_length |
Closed-tour length of a 1-based 'tour' (a permutation of '1:n_cities' - see this module's own doc, "Index-convention decision") on the instance named/parsed by 'name_or_text' (see ['load_tsp']), via ['Tsp::evaluate_batch']'s 'nint'-rounded 'EUC_2D' sum ('tsp.rs''s module doc). |
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