feat(ga): tournament selection + elitism

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

tests/unit/test_selection.py

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+import random
+
+from multi_swarm.ga.selection import elite_select, tournament_select
+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_tournament_picks_best_in_sample() -> None:
+    population = [make(i) for i in range(10)]
+    fitnesses = {g.id: float(i) for i, g in enumerate(population)}
+    rng = random.Random(0)
+    winner = tournament_select(population, fitnesses, k=5, rng=rng)
+    assert isinstance(winner, HypothesisAgentGenome)
+    assert fitnesses[winner.id] >= 0.0
+
+
+def test_tournament_size_one_is_random() -> None:
+    population = [make(i) for i in range(10)]
+    fitnesses = {g.id: float(i) for i, g in enumerate(population)}
+    rng = random.Random(0)
+    picks = [tournament_select(population, fitnesses, k=1, rng=rng) for _ in range(50)]
+    distinct = {p.id for p in picks}
+    assert len(distinct) > 1
+
+
+def test_elite_select_returns_top_k() -> None:
+    population = [make(i) for i in range(10)]
+    fitnesses = {g.id: float(i) for i, g in enumerate(population)}
+    elites = elite_select(population, fitnesses, k=3)
+    elite_fitnesses = sorted([fitnesses[g.id] for g in elites], reverse=True)
+    assert elite_fitnesses == [9.0, 8.0, 7.0]