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

@@ -0,0 +1,41 @@
+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]