Add (mu+lambda) evolutionary optimizer with Nelder-Mead polish (Stage 6 complete)

- optim/worker_context.py: shared per-process DB/bounds init, extracted
  from sweep.py so evolutionary.py doesn't reach into another module's
  private state
- optim/objective.py: build_run_record() shared between sweep and
  evolutionary so both write identically-shaped rows
- optim/evolutionary.py: tournament selection, whole-stage-swap crossover,
  elitism, structural mutation (stage count itself evolves), then a serial
  Nelder-Mead polish of the best genome's continuous parameters with
  discrete component choices frozen
- Found and fixed a real crash: crossover() indexed into an empty
  inter_stage_gaps_m list when both parents had only 1 stage (0 gaps),
  raising IndexError. Fixed the fallback to only choose from gaps that
  actually exist, defaulting to a neutral value (clipped later by
  repair()) when neither parent has one. Added a regression test.
- Verified 59/59 tests pass both locally and inside the Docker image

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

tests/test_evolutionary.py

@@ -0,0 +1,65 @@
+import random
+
+from gausse.components.database import ComponentDatabase
+from gausse.optim.evolutionary import polish_best, run_evolution
+from gausse.optim.search_space import SearchBounds, sample_genome
+from gausse.storage.database import count_runs, fetch_runs, open_connection
+
+DB = ComponentDatabase.load()
+
+
+def test_run_evolution_writes_all_evaluations_and_returns_summary(tmp_path):
+    db_path = tmp_path / "runs.sqlite3"
+    bounds = SearchBounds(max_stages=2)
+
+    summary = run_evolution(
+        db_path,
+        n_generations=2,
+        population_size=6,
+        bounds=bounds,
+        n_workers=2,
+        seed=7,
+        polish=False,
+    )
+
+    assert summary["n_evaluated"] == 12  # 2 поколения x 6 особей
+    assert "best_fitness" in summary
+
+    conn = open_connection(db_path)
+    assert count_runs(conn) == 12
+    rows = fetch_runs(conn)
+    assert all(r.search_mode == "evolve" for r in rows)
+    conn.close()
+
+
+def test_evolution_is_reproducible_given_same_seed(tmp_path):
+    bounds = SearchBounds(max_stages=2)
+    summary_a = run_evolution(
+        tmp_path / "a.sqlite3", n_generations=2, population_size=6, bounds=bounds,
+        n_workers=1, seed=99, polish=False,
+    )
+    summary_b = run_evolution(
+        tmp_path / "b.sqlite3", n_generations=2, population_size=6, bounds=bounds,
+        n_workers=1, seed=99, polish=False,
+    )
+    assert summary_a["best_fitness"] == summary_b["best_fitness"]
+    assert summary_a["best_efficiency"] == summary_b["best_efficiency"]
+
+
+def test_polish_does_not_make_the_best_genome_worse():
+    rng = random.Random(15)
+    bounds = SearchBounds(max_stages=2)
+    # ищем реализуемый геном как стартовую точку доводки
+    genome = None
+    for _ in range(30):
+        candidate = sample_genome(DB, bounds, rng)
+        from gausse.optim.objective import evaluate
+        result = evaluate(candidate, DB, bounds)
+        if result.feasible:
+            genome = candidate
+            baseline_fitness = result.fitness
+            break
+    assert genome is not None, "не нашли реализуемый геном за 30 попыток"
+
+    _, polished_result = polish_best(genome, DB, bounds)
+    assert polished_result.fitness >= baseline_fitness - 1e-9

tests/test_search_space.py

@@ -83,6 +83,22 @@ def test_crossover_produces_decodable_child():
         assert len(config.stages) == len(child.stages)
 
 
+def test_crossover_of_two_single_stage_genomes_does_not_crash():
+    """Регрессия: оба родителя с 1 ступенью (0 зазоров) роняли crossover
+    с IndexError при попытке взять запасной зазор из пустого списка."""
+    rng = random.Random(13)
+    a = sample_genome(DB, BOUNDS, rng)
+    b = sample_genome(DB, BOUNDS, rng)
+    a.stages = a.stages[:1]
+    a.inter_stage_gaps_m = []
+    b.stages = b.stages[:1]
+    b.inter_stage_gaps_m = []
+    for _ in range(20):
+        child = crossover(a, b, rng)
+        child = repair(child, DB, BOUNDS)
+        decode(child, DB, BOUNDS)
+
+
 def test_genome_dict_roundtrip():
     rng = random.Random(5)
     genome = sample_genome(DB, BOUNDS, rng)