Bruno Postle
ed2869074b
Implement a graded proximity comparator key (-n_fails, grade, fitness) behind a default-off use_grade flag: fitness._leaf_grade / score_with_grade sum f/FAIL_THRESHOLD over failing per-leaf quality factors; scalar fitness and fail count stay untouched so the inner-loop 0.5^n cliff (§5.4) is unaffected (0/9 regression check: PASS). Read once per child in driver._evaluate off the already-optimised tree; threaded through search_staged (Stage 2 only). Harbor staged A/B (20000 evals, seeds 0/1/2): lex 95/96/106 (mean 99.0) vs lex+grade 99/98/102 (mean 99.7) — grade wins 1/3, no plateau escape. Premise falsified: within a fixed fail-tier 0.5^n is constant so fitness still spans ~6 orders of magnitude; grade above fitness displaces that working signal. Verdict: reject; lexicographic (-n_fails, fitness) stands. Flag kept default-off for reproducibility / possible reuse as a §11.5 diversity signal. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> |
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| .. | ||
| test_dom_corpus.py | ||
| test_driver.py | ||
| test_fitness.py | ||
| test_genome.py | ||
| test_geometry.py | ||
| test_graph.py | ||
| test_innerloop.py | ||
| test_operators.py | ||
| test_oracle.py | ||
| test_staging.py | ||