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

include/fces/fitness.hpp

@@ -0,0 +1,90 @@
+#pragma once
+
+/**
+ * @file fitness.hpp
+ * @brief Fitness evaluation — loss signal processing and multi-objective evaluation.
+ *
+ * Port of: packages/fces/core/fitness_engine.py + fitness.py
+ */
+
+#include <cmath>
+#include <vector>
+
+namespace fces {
+
+/**
+ * Running statistics tracker (Welford's algorithm).
+ * Thread-safe, O(1) memory, numerically stable.
+ */
+class RunningStats {
+public:
+    void update(float value);
+    float z_score(float value) const;
+    float get_mean() const { return mean_; }
+    float get_std() const;
+    int get_count() const { return count_; }
+
+    void reset();
+
+private:
+    int count_ = 0;
+    float mean_ = 0.0f;
+    float m2_ = 0.0f;
+};
+
+/**
+ * FitnessEngine — processes raw loss values into controller fitness signals.
+ */
+class FitnessEngine {
+public:
+    explicit FitnessEngine(float grokking_coefficient = 0.1f);
+
+    /**
+     * Calculate loss velocity signal.
+     *
+     * @param current_loss Current step loss
+     * @param ema_loss Exponential moving average loss
+     * @param mode "relative" or "absolute"
+     * @return Velocity signal (negative = improving)
+     */
+    float calculate_loss_signal(float current_loss, float ema_loss, const std::string& mode = "relative") const;
+
+    /**
+     * Compute Kibble-Zurek Mechanism damping factor.
+     * Prevents topological defects during phase transitions.
+     */
+    float compute_kzm_damping(float spectral_alpha) const;
+
+private:
+    float grokking_coefficient_;
+};
+
+/**
+ * Fitness metrics for multi-objective evaluation.
+ */
+struct FitnessMetrics {
+    float loss_improvement = 0.0f;
+    float sparsity_score = 0.0f;
+    float stability_score = 0.0f;
+    float novelty_score = 0.0f;
+
+    /// Weighted combination
+    float total(float alpha = 0.7f, float beta = 0.3f) const {
+        return alpha * loss_improvement + beta * sparsity_score;
+    }
+};
+
+/**
+ * FuzzyFitnessEvaluator — multi-objective fitness evaluation with fuzzy weighting.
+ */
+class FuzzyFitnessEvaluator {
+public:
+    FitnessMetrics evaluate(
+        float loss_before,
+        float loss_after,
+        float sparsity = 0.0f,
+        float val_loss = -1.0f
+    ) const;
+};
+
+}  // namespace fces