Optimizer Card — nsga-ii v1.0.0

artifact_kind: optimizer-card artifact_name: nsga-ii version: 1.0.0 owner: Nous · agreement-search team last_reviewed: 2026-04-25 next_review: 2026-07-25 optimizer_kind: multi-objective related_phase_179_tasks: [179.4.2.2]#

1. Identity#

• Optimizer name: nsga-ii
• Algorithm family: NSGA-II (Deb et al. 2002) — non-dominated sorting + crowding-distance multi-objective evolutionary search.
• Implementation source: libs/nous/agreement-search/src/nsga-ii.ts plus shared GA primitives in ga-operators.ts.
• Intended use: approximating the Pareto frontier when parties or mediators must see genuinely different agreement families (60/40 vs 40/60, early-payout vs long-term royalty, restorative vs restitutive). Used by the workbench frontier view (§179.4.3.1).

2. Scoring rule#

• Utility aggregation: none — multi-objective. One objective per party by default (maximize each party's mean utility minus BATNA); callers may pass { id, direction } objectives to add fairness-metric or domain objectives.
• Fairness metrics reported: caller-selected; full fairness-metrics.ts set attaches to the run summary.
• Handling of hard constraints: filtered by filterCandidates before the candidate enters the population.
• Handling of uncertainty: objectives may consume posterior mean, LCB, or Thompson-sampled draws; the kernel is agnostic and uses whatever the caller's evaluate returns.

3. Inputs#

• Candidate space: same as nash-genetic — sandboxed clause-mutator DSL; same DEFAULT_ENABLED_MUTATORS.
• Initial population / seeds: §179.4.1.1 sources.
• Time / iteration budget: generations × population size; tournament selection on (rank, crowding) is binary by default.
• Randomness: seeded mulberry32.

4. Outputs#

• Accepted candidate contract: the entire final non-dominated front F_1 plus dominated fronts F_2, F_3, ..., each annotated with rank and crowding distance.
• Pareto-frontier diagnostics: rank histogram, per-front crowding distribution, dominated/dominating counts. The workbench renders these as the diversity panel (§179.4.3.1).
• Uncertainty propagation: every front member carries the same posterior detail nash-genetic emits.

5. Evaluation#

6. Known limitations#

• Crowding distance assumes objectives are roughly normalized; large scale mismatches between objectives degrade diversity preservation.
• (µ + λ) selection can still mode-collapse in pathological landscapes; pair with MAP-Elites if behavioural diversity is required.
• Mutation rate hyperparameters are caller-supplied; misconfigured rates can stall exploration.

7. Guardrails#

• Candidate safety gate: filterCandidates on every child.
• Clause static validation: §179.2.3.4 static validation inside filterCandidates.
• Redline separation: hard constraints filtered before the population.
• Pareto-coercion audit: the workbench rendering layer (pareto-explanations.ts) cannot reorder fronts to manipulate party perception; the rendering is from the kernel's record.

8. References#

• libs/nous/agreement-search/src/nsga-ii.ts
• Deb, Pratap, Agarwal, Meyarivan (2002), "A fast and elitist multiobjective genetic algorithm: NSGA-II".
• docs/research/bibliography.md §pareto-frontier