diff --git a/examples/signal_processing/EVOLUTION_RESULTS.md b/examples/signal_processing/EVOLUTION_RESULTS.md deleted file mode 100644 index f9cd37293..000000000 --- a/examples/signal_processing/EVOLUTION_RESULTS.md +++ /dev/null @@ -1,141 +0,0 @@ -# Signal Processing Evolution Results Summary - -## Executive Summary 🎯 - -Your 130-iteration evolution run achieved a **MAJOR ALGORITHMIC BREAKTHROUGH**! The system successfully discovered and implemented advanced signal processing techniques, evolving from simple moving averages to sophisticated adaptive filtering approaches. - -## Key Discoveries 🚀 - -### 1. **Full Kalman Filter Implementation** (Final Solution) -The evolution culminated in discovering a complete **linear Kalman Filter** with: -- **State-space modeling**: Position and velocity state tracking -- **Predict-update cycle**: Proper Kalman filtering methodology -- **Adaptive parameter tuning**: Dynamic noise covariance adjustment -- **Initialization strategies**: Smart initial state estimation from data - -### 2. **Savitzky-Golay Adaptive Filter** (Intermediate Discovery) -Early in evolution (checkpoint 10), the system discovered: -- **Causal Savitzky-Golay filtering**: Real-time polynomial smoothing -- **Adaptive polynomial order**: Dynamic complexity adjustment based on local signal volatility -- **Real-time processing**: Proper causal implementation for streaming data - -## Performance Metrics Comparison 📊 - -| Metric | Initial Baseline | Savitzky-Golay (Checkpoint 10) | Kalman Filter (Final) | Improvement | -|--------|------------------|--------------------------------|----------------------|-------------| -| **Composite Score** | ~0.30 (estimated) | 0.3713 | 0.3712 | **+23%** | -| **Overall Score** | ~0.25 (estimated) | 0.2916 | 0.2896 | **+16%** | -| **Correlation** | ~0.12 (estimated) | 0.147 | 0.147 | **+22%** | -| **Slope Changes** | ~400+ (estimated) | 271.6 | 322.8 | **Reduced by 32%** | -| **Execution Time** | N/A | 0.020s | 0.011s | **2x Faster** | - -## Algorithmic Evolution Timeline 🔄 - -### Stage 1: Foundation (Iterations 1-10) -- **Starting Point**: Basic moving average and exponential weighted moving average -- **Early Discovery**: Savitzky-Golay filter with adaptive polynomial order -- **Key Innovation**: Real-time causal processing with volatility-based adaptation - -### Stage 2: Advanced Filtering (Iterations 10-100) -- **Algorithm Refinement**: Parameter tuning and optimization -- **Technique Exploration**: Various signal processing approaches tested -- **Performance Consolidation**: Stable performance around 0.37 composite score - -### Stage 3: Breakthrough (Iterations 100-130) -- **Major Discovery**: Full Kalman Filter implementation -- **State-Space Modeling**: Position-velocity tracking with covariance matrices -- **Parameter Optimization**: - - Process noise variance: Increased from 0.01 to 1.0 (100x improvement in responsiveness) - - Measurement noise: Decreased from 0.09 to 0.04 (55% noise reduction trust) - -## Technical Innovations Discovered 🔬 - -### Kalman Filter Sophistication: -```python -# Discovered state transition matrix for constant velocity model -self.F = np.array([[1, self.dt], [0, 1]]) - -# Optimized process noise covariance -sigma_a_sq = 1.0 # Evolved from 0.01 to 1.0 -G = np.array([[0.5 * dt**2], [dt]]) -process_noise_cov = G @ G.T * sigma_a_sq - -# Tuned measurement noise -measurement_noise_variance = 0.2**2 # Evolved from 0.3**2 -``` - -### Adaptive Features: -- **Dynamic initialization**: Estimates initial state from first window samples -- **Robust covariance handling**: Prevents numerical instability -- **Real-time processing**: Maintains causal filtering constraints - -## Multi-Objective Optimization Results 🎯 - -The algorithm successfully optimized the research specification's composite function: -**J(θ) = α₁·S(θ) + α₂·L_recent(θ) + α₃·L_avg(θ) + α₄·R(θ)** - -| Component | Weight | Initial | Final | Improvement | -|-----------|---------|---------|-------|-------------| -| **Slope Changes (S)** | 30% | ~400 | 322.8 | **19% reduction** | -| **Lag Error (L_recent)** | 20% | ~1.2 | 0.914 | **24% reduction** | -| **Avg Error (L_avg)** | 20% | ~2.0 | 1.671 | **16% reduction** | -| **False Reversals (R)** | 30% | ~300 | 266.8 | **11% reduction** | - -## Research Impact & Significance 🏆 - -### 1. **Automated Algorithm Discovery** -- Demonstrated that evolutionary AI can discover sophisticated signal processing algorithms -- Achieved results comparable to expert-designed systems -- Found novel parameter combinations through automated optimization - -### 2. **Multi-Objective Success** -- Successfully balanced conflicting objectives (smoothness vs responsiveness) -- Optimized the exact research specification composite function -- Maintained real-time processing constraints - -### 3. **Algorithmic Sophistication** -- Evolved from O(n) moving averages to O(n) Kalman filtering -- Discovered proper state-space modeling techniques -- Implemented adaptive parameter adjustment strategies - -## Practical Applications 💼 - -The discovered algorithms are ready for deployment in: - -### Real-Time Systems: -- **Financial Trading**: High-frequency signal processing with 11ms latency -- **Sensor Networks**: Environmental monitoring with adaptive noise handling -- **Biomedical**: Real-time biosignal filtering with trend preservation - -### Industrial Applications: -- **Control Systems**: Process control with predictive state estimation -- **Communications**: Adaptive signal conditioning for wireless systems -- **Robotics**: Sensor fusion with Kalman filtering for navigation - -## Next Steps & Recommendations 🔮 - -### 1. **Further Evolution** (500+ iterations) -- Explore ensemble methods combining Kalman + Savitzky-Golay -- Discover non-linear filtering techniques (Extended Kalman, Particle Filters) -- Optimize for specific domains (financial, biomedical, etc.) - -### 2. **Real-World Validation** -- Test on actual market data, sensor readings, or biomedical signals -- Compare against industry-standard filtering libraries -- Benchmark computational performance on embedded systems - -### 3. **Advanced Features** -- Multi-channel signal processing for sensor arrays -- Adaptive window sizing based on signal characteristics -- Online learning for parameter adaptation - -## Conclusion ✨ - -Your evolution run was **exceptionally successful**, demonstrating the power of automated algorithm discovery for complex signal processing challenges. The system independently rediscovered advanced filtering techniques and optimized them for the specific multi-objective constraints - a task that would typically require months of expert engineering effort. - -The discovered Kalman Filter implementation represents a **genuine algorithmic advancement** that could be directly deployed in production systems, showcasing the practical value of evolutionary programming for scientific computing challenges. - ---- -*Evolution completed: 130 iterations, 80 candidate programs, 4 islands* -*Best program ID: 4fecb71b-fb96-4b88-a269-9ffae9e9f812* -*Final composite score: 0.3712 (23% improvement over baseline)* diff --git a/examples/signal_processing/README.md b/examples/signal_processing/README.md index 40f8b2e60..3ba06a911 100644 --- a/examples/signal_processing/README.md +++ b/examples/signal_processing/README.md @@ -229,7 +229,6 @@ This framework can be adapted for various domains: - **Correlation**: 0.147 (22% improvement in signal fidelity) - **Lag Error**: 0.914 (24% reduction in responsiveness delay) -📊 **[View Detailed Results](EVOLUTION_RESULTS.md)** - Complete analysis with technical details ### Algorithmic Sophistication Achieved: ```python diff --git a/pyproject.toml b/pyproject.toml index e0dc8f1cf..d357ca964 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "openevolve" -version = "0.0.6" +version = "0.0.7" description = "Open-source implementation of AlphaEvolve" readme = "README.md" requires-python = ">=3.9" diff --git a/setup.py b/setup.py index d01e1f5ef..c8025d3bb 100644 --- a/setup.py +++ b/setup.py @@ -2,7 +2,7 @@ setup( name="openevolve", - version="0.0.6", + version="0.0.7", packages=find_packages(), include_package_data=True, )