From 3f36ad65dd2724626bccd4bbeaa7bc8abbf65322 Mon Sep 17 00:00:00 2001
From: AdrianoDev <adrianodalpastro@gmail.com>
Date: Sat, 9 May 2026 20:12:51 +0200
Subject: [PATCH] feat(ga): tournament selection + elitism

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
---
 src/multi_swarm/ga/selection.py | 30 +++++++++++++++++++++++
 tests/unit/test_selection.py    | 42 +++++++++++++++++++++++++++++++++
 2 files changed, 72 insertions(+)
 create mode 100644 src/multi_swarm/ga/selection.py
 create mode 100644 tests/unit/test_selection.py

diff --git a/src/multi_swarm/ga/selection.py b/src/multi_swarm/ga/selection.py
new file mode 100644
index 0000000..df4b19c
--- /dev/null
+++ b/src/multi_swarm/ga/selection.py
@@ -0,0 +1,30 @@
+from __future__ import annotations
+
+import random
+
+from ..genome.hypothesis import HypothesisAgentGenome
+
+
+def tournament_select(
+    population: list[HypothesisAgentGenome],
+    fitnesses: dict[str, float],
+    k: int,
+    rng: random.Random,
+) -> HypothesisAgentGenome:
+    """Estrae k individui random e restituisce il migliore."""
+    if k < 1:
+        raise ValueError("k must be >= 1")
+    if not population:
+        raise ValueError("empty population")
+    candidates = rng.sample(population, k=min(k, len(population)))
+    return max(candidates, key=lambda g: fitnesses.get(g.id, 0.0))
+
+
+def elite_select(
+    population: list[HypothesisAgentGenome],
+    fitnesses: dict[str, float],
+    k: int,
+) -> list[HypothesisAgentGenome]:
+    """Restituisce i k genomi con fitness più alta."""
+    sorted_pop = sorted(population, key=lambda g: fitnesses.get(g.id, 0.0), reverse=True)
+    return sorted_pop[:k]
diff --git a/tests/unit/test_selection.py b/tests/unit/test_selection.py
new file mode 100644
index 0000000..dca01d5
--- /dev/null
+++ b/tests/unit/test_selection.py
@@ -0,0 +1,42 @@
+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]