Giorgio Gilestro
f5f68f5249
A new analytic experiment on an orthogonal evolution-of-sex axis: not the recombination RATE (E9) but the population's mating STRUCTURE. Agents on a ring recombine with a second parent drawn from a window of breadth b (b->0 monogamous/isolation-by-distance, b=1 promiscuous/panmictic), under local selection, swept against NK ruggedness K. Finding: the optimal mate-pool breadth SHRINKS as skills get more entangled. Wide/promiscuous merging wins the champion on additive landscapes (K<=3, b=0.6), but on rugged ones (K>=6) it prematurely converges to a worse champion and an intermediate breadth (b~0.35) wins; pure monogamy over-fragments. Throughout, promiscuity monotonically lifts the population MEAN but destroys diversity and parallel exploration. The design rule extends E9: merge widely for additive skills, keep island-structured sub-populations for entangled ones — a merging-native axis the panmixia-assuming literature lacks. - src/knowledge/mating_system.py + experiment.py dispatch (kind: mating_system) - configs/layer1/E14.yaml (breadth x K sweep, 20 reps, bitwise-reproducible) - figures/plot_E14.py; results/E14/ (figure, README, manifest, resolved config) - tests/test_mating_system.py (+5, 147 green); make layer1 wired - folded into both papers (full + accessible) as the third §5 result Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
47 lines
2.5 KiB
Makefile
47 lines
2.5 KiB
Makefile
# Layer 1 + Layer 1.5 automation. The uv venv (built from the committed uv.lock) is the
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# reproducibility source of truth; every target runs inside it via `uv run`.
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.PHONY: env env-neural env-mnist env-llm test layer1 layer2 neural mnist llm figures clean
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env: ## build .venv from the committed lockfile
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uv sync --extra dev
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env-neural: ## add the Layer 1.5 torch stack (GPU; Stage C onward)
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uv sync --extra dev --extra neural
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env-mnist: ## add torchvision for the real-MNIST confirmation tier
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uv sync --extra dev --extra neural --extra mnist
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test: ## correctness tests + scientific-validation tests (the spine of trust)
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uv run pytest
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layer1: ## run experiments E1-E6 + the learning-kernel bridge (analytic)
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for e in E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 E11 E12 E12_nk E14 kernel_sharpen kernel_smooth; do uv run python -m knowledge.experiment configs/layer1/$$e.yaml; done
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neural: ## run Layer 1.5 synthetic neural experiments (excludes the MNIST/torchvision tiers)
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for c in configs/neural/*.yaml; do case "$$c" in *mnist*|*speciation_real*) ;; \
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*) uv run python -m neural.experiment "$$c" ;; esac; done
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mnist: ## run the torchvision tiers: MNIST collapse + E13 real-weight speciation (needs env-mnist)
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uv run python -m neural.experiment configs/neural/mnist_collapse.yaml
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uv run python -m neural.experiment configs/neural/speciation_real.yaml
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uv run python -m neural.experiment configs/neural/speciation_real_cliff.yaml
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env-llm: ## add the LLM stack for the Layer-2 prototype (GPU; transformers/peft)
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uv sync --extra dev --extra neural --extra llm
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llm: ## run the LLM prototypes: merge (fusion) + moe (union) + directed (offspring select)
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uv run python -m llm.experiment configs/llm/merge.yaml
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uv run python -m llm.experiment configs/llm/moe.yaml
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uv run python -m llm.experiment configs/llm/directed.yaml
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layer2: neural ## alias: Layer 1.5 is the current Layer-2 deliverable (LLM rung deferred)
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figures: ## regenerate figures from committed results
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for e in E1 E2 E3 E4 E5 E6; do MPLBACKEND=Agg uv run python figures/plot_$$e.py; done
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for p in figures/plot_*.py; do case "$$p" in */plot_E[1-6].py|*/_*) ;; \
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*) [ -e "$$p" ] && MPLBACKEND=Agg uv run python "$$p" ;; esac; done
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clean: ## remove caches and generated results (keeps committed manifests)
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rm -rf .pytest_cache **/__pycache__
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find results -type f ! -name '.gitkeep' -delete 2>/dev/null || true
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