Giorgio Gilestro
48181a1c84
Deepen the sexual-reproduction frame before entering the full society, on
the two facets GG chose: landscape robustness and directed recombination.
Adds a Kauffman NK landscape (genotype.nk_fitness, tunable ruggedness),
finite n-parent crossover (genotype.crossover, per-gap recombination rate),
and hill-climb (parents = local optima = trained models).
E9 (recomb_landscape) -- the "why sex?" test: E8's dramatic super-parent
result used an ADDITIVE landscape. On rugged/epistatic landscapes, blindly
recombining local optima causes OUTBREEDING DEPRESSION -- offspring fall
below the parents, worse with both ruggedness and recombination rate (K=8,
free recomb: ~ -0.23), and the optimal recombination rate shrinks as
ruggedness grows. Design rule: merge freely when skills are complementary/
additive; sparingly (and with selection) when entangled.
E10 (directed_sex) -- directed sex beats biological sex: biology is stuck
with 2 random-mating parents and no offspring preview; an AI can choose
complementary mates, evaluate many recombinant offspring, keep the fittest,
and use unbounded parents (iterated recombine-then-select). Random
("biological") sex craters with ruggedness (0.66->0.51); directed sex
tracks/exceeds the best parent at every ruggedness -- converting the
outbreeding-depression catastrophe into a win. No biological analog.
Complete sexual-transmission picture: dramatic super-parent offspring when
skills are complementary (E8); outbreeding-depression risk when entangled
(E9); directed sex resolves the risk (E10). configs/layer1/{E9,E10}.yaml,
figures/plot_{E9,E10}.py, READMEs, +5 tests (117 green).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
27 lines
1.3 KiB
YAML
27 lines
1.3 KiB
YAML
experiment: E9
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kind: recomb_landscape
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seed: 20260705
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n_replicates: 24
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# (Landscape robustness / the "why sex?" question — the credibility centerpiece): E8 showed sex wins
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# on an ADDITIVE landscape, where recombination trivially helps. Does it survive EPISTASIS? Parents
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# are local optima ("trained models") of a Kauffman NK landscape whose ruggedness K (epistatic
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# interactions per locus) is swept with the recombination rate. Expect: on smooth/mildly-rugged
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# landscapes recombination helps; on rugged ones FREE recombination (rate~0.5) breaks co-adapted
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# blocks and offspring fall BELOW the parents (outbreeding depression); and the OPTIMAL recombination
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# rate shrinks as ruggedness grows. Design rule: merge freely when skills are complementary/additive;
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# merge sparingly when entangled. Falsifier: recombination rate has no effect, or free recombination
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# never underperforms the parents on rugged landscapes.
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society:
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L: 12
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n_parents: 6 # trained specialists = local optima of the landscape
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pop: 200 # recombinant offspring sampled per (K, rate, replicate)
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sweep:
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- param: K
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values: [0, 2, 4, 6, 8] # landscape ruggedness (epistasis): 0 = additive, high = rugged
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- param: rate
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values: [0.0, 0.05, 0.1, 0.2, 0.35, 0.5] # clonal -> free recombination
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output: {dir: results/E9}
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