Bruno Postle
0e5e607c4f
Bakeoff with native fitness shows NM wins at all DOF sizes: +9% at child_budget=80 for programme-house (6-7 DOF), and decisively at harbor-house scale (35-40 DOF) where CMA-ES exhausts its convergence detector after ~3 generations (46 evals) and adds failures on 12/15 runs. NM uses the full budget, is parameter-free, and has zero new failures across all test cases. - Add nm_search() to innerloop.py; change optimise() default to "nm" - Add nm_search to parametrised test cases - Add bakeoff_native.py and bakeoff_harbor.py experiments with results Co-Authored-By: Claude Sonnet 4.6 <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 | ||