Operators and selection

These items were open on the DEAP tracker, or were defects inherited from the original sources.

1. mut_polynomial_bounded clamps each gene into [low, up] before the Deb powers and requires eta > 0, so an out-of-box gene no longer writes NaN or a complex value.
2. cx_simulated_binary_bounded uses the same clamp and eta guard. Out-of-box parents no longer raise TypeError on complex arithmetic. The second child uses the opposite sign of \(\beta_q\), matching Deb / DEAP (c2 is the upper child, not a second lower child).
3. cx_blend_bounded is the boxed form of blend crossover: the same \(\gamma\) draw as cx_blend, then a clamp on each child gene.
4. cx_partially_matched, uniform PMX, and cx_ordered map alleles by value. Permutations of named cities or other non-{0..n-1} encodings no longer IndexError or silently write the wrong genes.
5. mut_heterogeneous applies one mutator per gene, so a mixed encoding (bit + int range + choice) does not need a one-off mutator. cx_heterogeneous is the matching mate dispatcher: one callable per gene, or one existing cx_* per slice.
6. assign_crowding_dist can crowd on wvalues via use_weights=True. The default, and sel_nsga_2, still use raw values.
7. sel_tournament_dcd accepts any 1 ≤ k ≤ len(individuals). When k is a multiple of 4 the original paired-shuffle path is used; other counts run pairwise contests until k winners are collected. k ≤ 0 returns an empty list.
8. sel_roulette and sel_stochastic_universal_sampling spin the wheel on wvalues[0]. A negative floor is shifted so a minimization weight still has a positive slice. When every slice is zero the draw is uniform instead of an empty list. An empty pool or sel_count ≤ 0 returns [].
9. mig_ring evicts by object identity. Two individuals with equal genes are no longer treated as the same slot. Emigrants are cloned when a replacement operator is set, so the source deme is not aliased into the destination. Duplicate draws take the next unused index instead of writing one vacancy twice.
10. cx_messy_one_point cuts each parent independently, so lengths can change. Equal-length parents no longer degenerate into a shared-interval two-point swap.
11. Sequence crossovers copy slices before assignment. NumPy views are not aliased, so a one-point or uniform swap does not destroy a parent. cx_es_two_point_copy applies the same copy to the strategy vector.
12. sel_spea_2 uses the full distance row for density, with the self-distance set to infinity, so the \(k\)-th neighbour is not the zero pad of the upper triangle. An empty pool returns [].
13. sel_nsga_3 intercepts on the success path are \(1/x + \mathrm{best}\). Association treats a near-zero \(\mathrm{intercepts} - \mathrm{best}\) gap as \(1\) so the niche distance is not NaN. Niching stops when the last front is exhausted, so a \(k\) larger than the pool does not loop forever.
14. cx_one_point, cx_two_point, cx_ordered, and mut_shuffle_indexes no-op when a parent is shorter than two genes, so a length-1 individual no longer hits an empty randint interval or a two-cut sample on a one-gene permutation.
15. broadcast_param treats numbers.Integral and numbers.Real as scalars, so a NumPy integer or float32 / float16 bound no longer raises TypeError from len() on a NumPy scalar.
16. sel_lexicase and sel_epsilon_lexicase accept cases= to filter on a per-generation subset of fitness indices. Defaults still use every case. sample_informed_cases builds that subset by farthest-first traversal of Hamming distances between case solve vectors, so synonymous cases are not over-sampled. fitness_case_matrix packs fitness.values into a dense matrix; optional matrix= and trust_matrix= let lexicase and informed down-sampling reuse one pack per generation.
17. sel_sms_emoa reduces a pool by non-dominated sorting, then removes the least hypervolume contributor on the critical front until the quota is met. Optional ref_point follows the same minimization-space convention as hypervolume and least_contrib. Works on parents + offspring or steady-state parents + [child].
18. sel_moead and SelMOEADWithMemory pick one winner per decomposition weight from uniform_reference_points, using Tchebycheff or PBI scalarization with Pareto-rank-aware tie breaks and NSGA-II-style crowding on the fill pass.
19. sel_age_moea_2 and SelAGE2WithMemory advance front by front: geodesic diversity on partial \(F_1\), inverse Minkowski on later partial fronts, with Newton–Raphson curvature on the first front.
20. mut_gaussian_bounded applies the same \(N(\mu, \sigma)\) draw as mut_gaussian, then clamps each mutated gene into [low, up]. Bounds may be scalars or per-gene sequences, matching mut_polynomial_bounded. Empty intervals are skipped.
21. mut_de writes a DE/rand/1/bin trial in place: \(a_i + F(b_i - c_i)\) on a binomial gene subset (cx_prob, at least one gene). Optional low / up clamp the written genes. Selection stays on the caller. DEAP only has this loop in examples.
22. constraint_dominates is Deb's feasibility-first comparison: feasible beats infeasible; two feasibles use ordinary Pareto; two infeasibles prefer the smaller constraint violation. sel_nsga_2 accepts optional feasible= / violation= callables and ranks with that rule. Defaults stay unconstrained NSGA-II. Fitness values are not rewritten.
23. sel_team assembles sel_count individuals by greedy maximum coverage of cases solved at 0. Optional cases=, matrix=, and trust_matrix= match lexicase. The team is a sequence of pool members; member fitness is not rewritten. Team scoring stays on the caller. sel_team_archive pools occupied MAP-Elites cells via list(archive) and delegates to sel_team. DEAP has no team selector.
24. CaseExam and CaseExamPool store case subsets as ranges or a 1-D bool mask — the same shapes case_errors and sel_lexicase(..., cases=) consume — not as a new genome. score_case_exams ranks exams on elites by unsolved count or Hamming distance from the all-solved vector (solved ≡ \(0\)). mut_case_ranges / mut_case_mask vary bounds or flip mask runs; guard_case_exams blocks the empty exam and the all-solved collapse. next_lexicase_cases returns the mutated or guarded winner as the next cases= list. informed=True (the default) is a guard repair path only — it does not overwrite a healthy exam. Chronological splits stay on the caller.
25. mig_ring pairs source emigrants with dest vacancies by length, and stops claiming slots once a deme is full. Unequal island sizes, or sel_random with k larger than a deme, no longer IndexError or StopIteration. An unreplaced home vacancy clones the leftover emigrant so two demes do not share one object.
26. sel_best and sel_worst return [] when sel_count <= 0. A negative count is no longer a Python slice that drops individuals from the other end of the ranked list.
27. mig_ring clones an emigrant that is already present in the destination, so a selector that returns the same object twice does not write that object into two dest slots.
28. sel_spea_2 returns [] when sel_count <= 0. A negative count no longer enters archive truncation and IndexErrors on an empty list. Matches sel_nsga_2 and sel_best.
29. cx_partially_matched no-ops when a parent is shorter than two genes. Empty permutations no longer hit randint(0, -1).
30. broadcast_param treats a 0-d ndarray as a scalar, so numpy.array(0.0) bounds no longer raise TypeError from len() on an unsized object.
31. policy_held_out_fitness scores policy individuals only on a caller-marked held_out exam. guard_policy_fitness_exam refuses train exams or a freshly mutated exam as the objective. Train quality stays in policy_exam_scores / policy_observe; record_policy_generalization_gap logs train, held-out, and gap as a generalization_gap Logbook chapter (Push GP P13).
32. PolicyActionGuard and guard_policy_action enforce hard caps on apply_policy_action: max promotes per generation, promote cooldown, max inner tune generations, minimum exam size, and rejection when an action would exceed remaining n_evals. Rejected actions return PolicyActionResult(rejected=True) without raising — the observation surface for last_action_rejected. DEAP has no policy action firewall (Push GP P14).
33. sel_novelty ranks a pool by average distance to the k nearest archive behavior descriptors via semantic_distance. Fitness stays on ind.fitness; novelty is the selection key. An empty archive falls back to sel_random. mut_iso_line interpolates toward a donor elite with t ~ Uniform(-iso, 1 + iso) and adds isotropic noise; iso_line_float, iso_line_int, and iso_line_bit compose through mut_heterogeneous on mixed genomes. ea_map_elites registers both like any other select / mutate; random_elites stays the parent source. DEAP has no novelty selector or archive-aware iso+line mutator. See the MAP-Elites example.
34. mig_fully_connected and mig_random sit next to mig_ring. Fully connected selects emigrants once per source, clones them along each outgoing edge, and claims distinct destination vacancies across incoming edges; random picks one destination per source. island_eval_keys hashes each deme's CaseExam — catalog subsets via a painted mask so mask and range storage match — and optional matrix identity for step_islands(..., eval_keys=). Custom graphs stay mig_ring(..., mig_indices=). DEAP has no named topologies. See the multiprocessing tutorial.
35. sel_batch_epsilon_lexicase shuffles active cases into batches of at most batch_size, reduces each batch (mean squared error by default), and runs epsilon-lexicase on the shorter matrix. A fresh partition is drawn per selected individual. Optional reduction= overrides the batch aggregate. matrix= / trust_matrix= / cases= match lexicase. sel_tournament_cases scores individuals on a case subset (column mean by default), then tournaments on those scalars. Informed down-sampling stays on sample_informed_cases; case_count= draws a random subset when cases is omitted. DEAP has no batch epsilon-lexicase or case-subset tournament. See the columnar GP tutorial.
36. sel_epsilon_lexicase accepts mode= on the vectorized filter: epsilon_auto / epsilon_static (population MAD and elite), epsilon_semi (population MAD, pool elite), and epsilon_dynamic (pool MAD and elite). next_downsample_cases returns the next cases= list each generation with mode= random, informed, cohort, or held_out (rotate through a caller-marked held-out exam). Chronological meaning stays on the caller. DEAP has no filter-pool epsilon modes or generation downsample schedule. See the columnar GP tutorial.
37. mut_iso_line treats NumPy integer genes as integers. A numpy.int64 gene no longer takes the float path and writes a non-integer value.
38. sel_random returns [] when the pool is empty. A positive sel_count no longer IndexErrors on rng.choice([]).
39. sel_double_tournament returns [] when the pool is empty or rounds <= 0. It no longer ValueErrors on max([]) after sel_random started returning an empty draw.