feat(genome): deterministic mutation operators (numeric + categorical)

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

src/multi_swarm/genome/mutation.py

@@ -0,0 +1,77 @@
+from __future__ import annotations
+
+import random
+from typing import Any
+
+from .hypothesis import HypothesisAgentGenome
+
+FEATURE_POOL: tuple[str, ...] = ("open", "high", "low", "close", "volume")
+
+COGNITIVE_STYLES: tuple[str, ...] = (
+    "physicist",
+    "biologist",
+    "historian",
+    "meteorologist",
+    "ecologist",
+    "engineer",
+)
+
+
+def _clone_with(g: HypothesisAgentGenome, **overrides: Any) -> HypothesisAgentGenome:
+    payload: dict[str, Any] = g.to_dict()
+    payload.update(overrides)
+    payload.pop("id", None)
+    payload["parent_ids"] = [*g.parent_ids, g.id]
+    payload["generation"] = g.generation + 1
+    return HypothesisAgentGenome.from_dict(payload)
+
+
+def mutate_temperature(g: HypothesisAgentGenome, rng: random.Random) -> HypothesisAgentGenome:
+    delta = rng.choice([-0.1, 0.1])
+    new_t = max(0.6, min(1.3, g.temperature + delta))
+    return _clone_with(g, temperature=round(new_t, 4))
+
+
+def mutate_lookback(g: HypothesisAgentGenome, rng: random.Random) -> HypothesisAgentGenome:
+    delta = rng.choice([-50, 50])
+    new_lb = max(50, min(500, g.lookback_window + delta))
+    return _clone_with(g, lookback_window=new_lb)
+
+
+def mutate_feature_access(g: HypothesisAgentGenome, rng: random.Random) -> HypothesisAgentGenome:
+    current = set(g.feature_access)
+    if len(current) == len(FEATURE_POOL):
+        op = "remove"
+    elif len(current) <= 1:
+        op = "add"
+    else:
+        op = rng.choice(["add", "remove"])
+
+    if op == "add":
+        candidates = [f for f in FEATURE_POOL if f not in current]
+        choice = rng.choice(candidates)
+        new_set = current | {choice}
+    else:
+        choice = rng.choice(sorted(current))
+        new_set = current - {choice}
+
+    return _clone_with(g, feature_access=sorted(new_set))
+
+
+def mutate_cognitive_style(g: HypothesisAgentGenome, rng: random.Random) -> HypothesisAgentGenome:
+    candidates = [s for s in COGNITIVE_STYLES if s != g.cognitive_style]
+    new_style = rng.choice(candidates)
+    return _clone_with(g, cognitive_style=new_style)
+
+
+MUTATION_OPS = (
+    mutate_temperature,
+    mutate_lookback,
+    mutate_feature_access,
+    mutate_cognitive_style,
+)
+
+
+def random_mutate(g: HypothesisAgentGenome, rng: random.Random) -> HypothesisAgentGenome:
+    op = rng.choice(MUTATION_OPS)
+    return op(g, rng)