MachineSex/results/E9/resolved_config.yaml
Giorgio Gilestro 48181a1c84 society: make the sexual-transmission model rigorous (E9 epistasis, E10 directed sex)
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>
2026-07-05 11:13:37 +01:00

30 lines
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YAML

experiment: E9
seed: 20260705
n_replicates: 24
source_config:
experiment: E9
kind: recomb_landscape
seed: 20260705
n_replicates: 24
society:
L: 12
n_parents: 6
pop: 200
sweep:
- param: K
values:
- 0
- 2
- 4
- 6
- 8
- param: rate
values:
- 0.0
- 0.05
- 0.1
- 0.2
- 0.35
- 0.5
output:
dir: results/E9