Pin what is actually invariant vs DEAP: non-dominated rank always identical (incl.
ties); crowding values+ordering and selNSGA2 survivor profile identical on
continuous fitness (within 1e-9); survivor rank-distribution identical under heavy
ties (the exact tied-individual kept is arbitrary in DEAP too). Drops the
over-strict exact-set/profile-under-duplicates claims that don't hold (boundary
points tie at inf crowding even for continuous objectives).

…ence history

Visualization (SeqOptPlot): new convergence (per-generation hypervolume + spread +
per-objective best, from the new SeqOpt.history_), 3-D pareto_front (optional z),
and parallel_coordinates for many-objective fronts. Per-generation history is now
tracked (spread + per-objective best, not only hypervolume) and exposed as
SeqOpt.history_.

Objectives: a callable source now receives the variant SEQUENCE (fn(sequence)->
float) and is cached per distinct variant, so any external predictor — a scikit/
torch model or a sequence-level tool / web API — can be optimized jointly with the
model-on-features objectives; pure-callable multi-objective runs need no CPP model.

Two executed example notebooks (seqopt_convergence, seqopt_parallel_coordinates)
demonstrate the views + the external-predictor recipe. 53 SeqOpt frontend tests
(+19) and 459 in the broader gate green.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

Rewrite all six SeqOpt example notebooks around a real task — 'design a super
gamma-secretase substrate': load_features('DOM_GSEC') (150 CPP features) +
load_dataset('DOM_GSEC') + a simple RandomForest, take a non-substrate wild-type
and mutate its TMD to maximize predicted substrate probability with few mutations.
They demonstrate run (nsga2/greedy, impact/importance, varOr/ea_simple/operators,
constraints + Hall of Fame, external-predictor callable objective), eval
(hypervolume/spread/convergence), and all four SeqOptPlot views (pareto_front 2-D/
3-D, parallel_coordinates, convergence, hypervolume), with executed outputs.

Fix (found via the realistic reference): SeqOpt mode='impact' refit kept the FULL
df_seq_ref, so a reference from load_dataset (carrying jmd_n/tmd/jmd_c/label) NaN-
tripped check_df_seq on the appended variant row. Now keep only the position-based
columns; add a regression test with an extra-column reference.

460-test broad gate + docstrings clean.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

…hero plots

Critical-assessment improvements:
- engine='fast' non-dominated sort now computes the dominance matrix in row-chunks
  (adaptive block), bounding the transient to O(block*n*m) vs O(n^2*m) — ~2.6x
  leaner peak memory at n=3000; identical fronts (parity unchanged). Realistic
  pool sizes were never a problem; this makes pathologically large populations safe.
- run() keeps a cumulative non-dominated archive (DEAP ParetoFront analogue),
  merged into the final population so the returned rank=0 front is the best-ever
  set — no solution lost to per-generation crowding truncation.
- history_ now tracks per-objective best/mean/worst per generation.

Hero plots (the genre's standard views):
- SeqOptPlot.mutation_map — position x amino-acid substitution-enrichment heatmap
  across the front (the directed-evolution 'which mutations won' view).
- SeqOptPlot.convergence gains the classic GA best/mean/worst fitness band.

New executed notebook seqopt_mutation_map; tests for mutation_map + the band +
archive. 465-test broad gate + docstrings clean.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

- SeqOptPlot.genealogy: mutational-lineage tree (wild-type -> variants by accumulated
  mutations, linked by mutation-set containment, colored by the first objective) - the
  directed-evolution analogue of a genealogy tree, matplotlib-only (no networkx).
- SeqOpt class docstring now carries a rendered list-table mapping every run/eval
  method + parameter value to its DEAP function (selNSGA2/varAnd/varOr/eaMuPlusLambda/
  cxUniform/DeltaPenalty/...), with the aaanalysis-only rows called out.
- New executed seqopt_genealogy notebook + tests. 273-test gate + docstrings clean.

…onsistency)

pareto_front / parallel_coordinates / mutation_map / genealogy take a user-overridable
cmap= (defaults unchanged), matching the CPPPlot / AAMutPlot / SeqMutPlot convention of
colormap-as-parameter instead of a hardcoded name.

tutorial7_protein_design: an executed end-to-end case study — train a GSEC substrate
classifier, design a 'super substrate' from a non-substrate, and read the result with
every SeqOptPlot view (pareto_front 2-D/3-D, convergence, mutation_map, genealogy,
parallel_coordinates) plus SHAP-guided impact mode. Wired into the Tutorials toctree
under a new Protein Design section.

…) + refs

Draw the paradigm distinction clearly in the SeqOpt class docstring, the tutorial,
and CONTEXT.md: SeqOpt does protein *engineering* — machine-learning-guided directed
evolution of an existing sequence [Yang19] — explicitly NOT de novo protein design
(generating new proteins). Introduce de novo design as the contrasting paradigm via
the canonical structure-first pipeline RFdiffusion [Watson23] -> ProteinMPNN
[Dauparas22] -> AlphaFold [Jumper21], reviewed in [DeNovoReview26]. Add all five
references to references.rst; tutorial retitled 'Protein Engineering with SeqOpt'
with the distinction + hyperlinked refs; Tutorials toctree section renamed.

Docstring citations resolve (0 defects); 104-test gate green.

…ce reviews

Read the two provided reviews and fixed the citations: ML-guided directed evolution
is Wittmann, Johnston, Wu & Arnold (2021), Curr. Opin. Struct. Biol. (not the Yang19
I had guessed); the de novo design review is Yang et al. (2026), Nature 652:1139.
Sharpened the distinction in the SeqOpt docstring + tutorial + CONTEXT.md using the
reviews' own framing (de novo = build new proteins from the ground up; engineering =
iterative mutation/selection of an existing protein, ML learns the fitness model).
Citations resolve; 64-test gate green.

…ed evolution

Add [Yang19] (Nature Methods 2019, the foundational ML-guided directed-evolution-for-
protein-engineering review) alongside [Wittmann21] in the SeqOpt docstring, tutorial
and CONTEXT.md. Citations resolve.

…rity

# Conflicts:
#	docs/source/index/release_notes.rst

…erators decision

Number the previously number-less parity ADR (one past the current master max 0044 =
find-features protocol), set status Accepted, regenerate INDEX.