{
  "schema": "vamos.documentation.coverage-evidence.v1",
  "python": "3.12.14",
  "packages": {
    "vamos-optimization": "1.0.1",
    "numpy": "2.1.3",
    "scipy": "1.15.3",
    "joblib": "1.4.2",
    "moocore": "0.3.2",
    "matplotlib": "3.10.9",
    "scikit-learn": "1.7.2",
    "numba": "0.67.0"
  },
  "base_commit": "24e601bebdc761b006facb3f784e2b620ac9532a",
  "command": "python tools/build_coverage_tutorials.py --write --work-dir <new-directory>",
  "retention": "Derived teaching arrays and observations; canonical ablation studies and original tuner artifacts remain in the work directory.",
  "sources": {
    "examples/tutorials/capability_examples.py": "cfe6fe0b17868882b6b3924a900b03c6a58c4df9caa308dd9ef9ca78ecec7722",
    "examples/tutorials/advanced_workflows.py": "5af030c83a5a7f8d08432b017024362b4d19291a8bf542176badf914fd10bcbb"
  },
  "outputs": {
    "smsemoa": "Algorithm: smsemoa; encoding: real; backend: numpy; seed: 42\nReturned X: (40, 10); F: (40, 2); result mode: population\nFeasible rows: 40/40; feasible non-dominated rows: 13\nEvaluations: 400\n",
    "spea2": "Algorithm: spea2; encoding: real; backend: numpy; seed: 42\nReturned X: (40, 10); F: (40, 2); result mode: population\nFeasible rows: 40/40; feasible non-dominated rows: 16\nEvaluations: 400\n",
    "ibea": "Algorithm: ibea; encoding: real; backend: numpy; seed: 42\nReturned X: (40, 10); F: (40, 2); result mode: population\nFeasible rows: 40/40; feasible non-dominated rows: 15\nEvaluations: 400\n",
    "smpso": "Algorithm: smpso; encoding: real; backend: numpy; seed: 42\nReturned X: (40, 10); F: (40, 2); result mode: population\nFeasible rows: 40/40; feasible non-dominated rows: 9\nEvaluations: 400\n",
    "agemoea": "Algorithm: agemoea; encoding: real; backend: numpy; seed: 42\nReturned X: (40, 12); F: (40, 3); result mode: population\nFeasible rows: 40/40; feasible non-dominated rows: 40\nEvaluations: 400\n",
    "rvea": "Algorithm: rvea; encoding: real; backend: numpy; seed: 42\nReturned X: (10, 12); F: (10, 3); result mode: population\nFeasible rows: 10/10; feasible non-dominated rows: 10\nEvaluations: 400\n",
    "binary": "Algorithm: nsgaii; encoding: binary; backend: numpy; seed: 42\nReturned X: (40, 80); F: (40, 2); result mode: population\nFeasible rows: 40/40; feasible non-dominated rows: 14\nEvaluations: 400\nDomain checks: passed for binary\n",
    "integer": "Algorithm: nsgaii; encoding: integer; backend: numpy; seed: 42\nReturned X: (40, 20); F: (40, 2); result mode: population\nFeasible rows: 40/40; feasible non-dominated rows: 40\nEvaluations: 400\nDomain checks: passed for integer\n",
    "permutation": "Algorithm: nsgaii; encoding: permutation; backend: numpy; seed: 42\nReturned X: (40, 6); F: (40, 2); result mode: population\nFeasible rows: 40/40; feasible non-dominated rows: 40\nEvaluations: 400\nDomain checks: passed for permutation\n",
    "mixed": "Algorithm: nsgaii; encoding: mixed; backend: numpy; seed: 42\nReturned X: (40, 9); F: (40, 2); result mode: population\nFeasible rows: 40/40; feasible non-dominated rows: 17\nEvaluations: 400\nDomain checks: passed for mixed\n",
    "engineering": "Algorithm: nsgaii; encoding: mixed; backend: numpy; seed: 42\nReturned X: (40, 6); F: (40, 2); result mode: population\nFeasible rows: 40/40; feasible non-dominated rows: 40\nEvaluations: 400\nMinimum-cost feasible front row: 0\nDecision (h, l, t, b, material, stiffeners): [0.783541, 4.169584, 1.760651, 3.396257, 0.0, 2.0]\nObjectives (cost, deflection proxy): [15.905242, 0.059214]\nConstraint residuals G <= 0: [-9827.611456, -5182.606983, -0.190786, -2.612716]\n",
    "feature_selection": "Algorithm: nsgaii; encoding: binary; backend: numpy; seed: 42\nReturned X: (16, 30); F: (16, 2); result mode: population\nFeasible rows: 16/16; feasible non-dominated rows: 4\nEvaluations: 80\nDataset: breast_cancer; split random_state: 0; validation fraction: 0.3\nNonempty returned feature masks: 16\nError is a repeatedly inspected validation objective, not an unbiased test estimate.\n",
    "model_tuning": "Algorithm: nsgaii; encoding: mixed; backend: numpy; seed: 42\nReturned X: (16, 4); F: (16, 2); result mode: population\nFeasible rows: 16/16; feasible non-dominated rows: 9\nEvaluations: 80\nDataset: wine; split random_state: 0; validation fraction: 0.3\nDecisions: log-scaled C, log-scaled gamma, kernel category, polynomial degree.\nObjectives: validation error; support-vector count + 0.1 * degree.\n",
    "tuning": "Experimental tuner: random; trials: 2; budget per run: 400\nBest training HV: 10.281806\nSeeds: {'train_seeds': [7], 'validation_seeds': [101, 102], 'test_seeds': [201, 202]}\nvalidation: 1 candidate summaries; successful evaluations of candidates: 2/2\ntest: 3 candidate summaries; successful evaluations of candidates: 6/6\nThe same ZDT1 instance is reused across splits; held-out seeds are not held-out problems.\n",
    "ablation": "Planned tasks: 6; evaluations: 2400\nbaseline: seed 11; evaluations 400; HV 10.912801\nbaseline: seed 22; evaluations 400; HV 10.843059\nbaseline: seed 33; evaluations 400; HV 10.841080\nwithout-mutation: seed 11; evaluations 400; HV 10.885577\nwithout-mutation: seed 22; evaluations 400; HV 10.552095\nwithout-mutation: seed 33; evaluations 400; HV 10.675069\nOne removed component, three seeds, one problem: descriptive evidence only.\n",
    "performance": "numpy: first 0.018157s; subsequent median 0.015707s; range [0.015278, 0.015735]s\nnumba: first 0.226595s; subsequent median 0.004910s; range [0.004663, 0.005652]s\nmoocore: first 0.008319s; subsequent median 0.006352s; range [0.006193, 0.006411]s\nActual evaluations: 4800; fresh optimizer state for every call.\nTimes describe this machine and tiny workload; no universal backend ranking follows.\n"
  },
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