feat: scaffold FCES-native C++ project with libtorch integration

- CMakeLists.txt with libtorch, GoogleTest, GoogleBenchmark, OpenMP, pybind11
- Header files: config, controller, population, fitness, evolution, spectral, oscillation, telemetry, optimizer
- Source implementations: controller (full micro-MLP forward pass, mutation, crossover), fitness (Welford's algorithm), oscillation (DFT), spectral (SVD rank), optimizer (sign-SGD stub)
- Tests: controller, population, fitness, optimizer (Google Test)
- Benchmarks: evolve throughput, optimizer step (Google Benchmark)
- Examples: simple optimization, PyTorch/libtorch integration
- Python extension: pybind11 bindings with setup.py
- README with architecture diagram and build instructions

tests/test_fitness.cpp

@@ -0,0 +1,33 @@
+#include <gtest/gtest.h>
+#include "fces/fitness.hpp"
+
+using namespace fces;
+
+TEST(RunningStatsTest, BasicUpdate) {
+    RunningStats stats;
+    stats.update(1.0f);
+    stats.update(2.0f);
+    stats.update(3.0f);
+    EXPECT_NEAR(stats.get_mean(), 2.0f, 1e-5f);
+    EXPECT_GT(stats.get_std(), 0.0f);
+}
+
+TEST(RunningStatsTest, ZScore) {
+    RunningStats stats;
+    for (int i = 0; i < 100; ++i) stats.update(static_cast<float>(i));
+    float z = stats.z_score(50.0f);
+    EXPECT_NEAR(z, 0.0f, 0.1f);
+}
+
+TEST(FitnessEngineTest, LossSignal) {
+    FitnessEngine engine;
+    float sig = engine.calculate_loss_signal(1.0f, 2.0f, "relative");
+    EXPECT_LT(sig, 0.0f);  // Improving
+}
+
+TEST(FitnessEngineTest, KZMDamping) {
+    FitnessEngine engine(0.1f);
+    float d = engine.compute_kzm_damping(5.0f);
+    EXPECT_GT(d, 0.0f);
+    EXPECT_LT(d, 1.0f);
+}