feat(ga): next_generation step (elitism + tournament + mutate/crossover)

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

src/multi_swarm/ga/loop.py

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+from __future__ import annotations
+
+import random
+from dataclasses import dataclass
+
+from ..genome.crossover import uniform_crossover
+from ..genome.hypothesis import HypothesisAgentGenome
+from ..genome.mutation import random_mutate
+from .selection import elite_select, tournament_select
+
+
+@dataclass(frozen=True)
+class GAConfig:
+    population_size: int
+    elite_k: int
+    tournament_k: int
+    p_crossover: float
+
+
+def next_generation(
+    population: list[HypothesisAgentGenome],
+    fitnesses: dict[str, float],
+    cfg: GAConfig,
+    rng: random.Random,
+) -> list[HypothesisAgentGenome]:
+    """Costruisce la prossima generazione: elitismo + tournament + crossover/mutate."""
+    new_pop: list[HypothesisAgentGenome] = list(
+        elite_select(population, fitnesses, cfg.elite_k)
+    )
+
+    while len(new_pop) < cfg.population_size:
+        if rng.random() < cfg.p_crossover and len(population) >= 2:
+            p1 = tournament_select(population, fitnesses, cfg.tournament_k, rng)
+            p2 = tournament_select(population, fitnesses, cfg.tournament_k, rng)
+            child = uniform_crossover(p1, p2, rng)
+        else:
+            parent = tournament_select(population, fitnesses, cfg.tournament_k, rng)
+            child = random_mutate(parent, rng)
+        new_pop.append(child)
+
+    return new_pop[: cfg.population_size]

tests/unit/test_ga_loop.py

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+import random
+
+from multi_swarm.ga.loop import GAConfig, next_generation
+from multi_swarm.genome.hypothesis import HypothesisAgentGenome, ModelTier
+
+
+def make(idx: int) -> HypothesisAgentGenome:
+    return HypothesisAgentGenome(
+        system_prompt=f"p-{idx}",
+        feature_access=["close"],
+        temperature=0.9,
+        top_p=0.95,
+        model_tier=ModelTier.C,
+        lookback_window=100,
+        cognitive_style="x",
+    )
+
+
+def test_next_generation_size_preserved() -> None:
+    population = [make(i) for i in range(20)]
+    fitnesses = {g.id: float(i) for i, g in enumerate(population)}
+    cfg = GAConfig(population_size=20, elite_k=2, tournament_k=3, p_crossover=0.5)
+    new_pop = next_generation(population, fitnesses, cfg, rng=random.Random(0))
+    assert len(new_pop) == 20
+
+
+def test_next_generation_includes_elites() -> None:
+    population = [make(i) for i in range(20)]
+    fitnesses = {g.id: float(i) for i, g in enumerate(population)}
+    cfg = GAConfig(population_size=20, elite_k=2, tournament_k=3, p_crossover=0.5)
+    new_pop = next_generation(population, fitnesses, cfg, rng=random.Random(0))
+    elite_ids = {
+        g.id for g in sorted(population, key=lambda g: fitnesses[g.id], reverse=True)[:2]
+    }
+    new_ids = {g.id for g in new_pop}
+    assert elite_ids.issubset(new_ids)
+
+
+def test_next_generation_increments_generation_for_offspring() -> None:
+    population = [make(i) for i in range(20)]
+    fitnesses = {g.id: float(i) for i, g in enumerate(population)}
+    cfg = GAConfig(population_size=20, elite_k=2, tournament_k=3, p_crossover=0.5)
+    new_pop = next_generation(population, fitnesses, cfg, rng=random.Random(0))
+    new_offspring = [g for g in new_pop if g.id not in {p.id for p in population}]
+    assert all(g.generation > 0 for g in new_offspring)