signal_processing

Real-Time Adaptive Signal Processing Algorithm Evolution

This example demonstrates how to use OpenEvolve to automatically discover and optimize real-time signal processing algorithms for non-stationary time series data. The challenge involves developing algorithms that can filter volatile signals while preserving important dynamics and minimizing computational latency.

Problem Overview

The Challenge

We need to develop a real-time signal processing algorithm that can:

1. Filter noise from volatile, non-stationary time series data
2. Preserve genuine signal dynamics and trend changes
3. Minimize spurious directional reversals caused by noise
4. Achieve near-zero phase delay for real-time applications
5. Operate efficiently within computational constraints

Input Signal Characteristics

• Type: Univariate time series (1D array of real-valued samples)
• Properties:
  • Non-linear dynamics
  • Non-stationary statistical properties
  • Aperiodic (non-seasonal) behavior
  • High frequency variability and volatility
  • Rapidly changing spectral characteristics

Technical Constraints

• Causal Processing: Must use finite-length sliding window
• Fixed Latency: Output length = Input length - Window size
• Real-time Capability: Process samples as they arrive
• Memory Efficiency: Bounded memory usage

Multi-Objective Optimization Framework

The algorithm performance is evaluated using a composite metric based on the research specification:

Optimization Function

J(θ) = α₁·S(θ) + α₂·L_recent(θ) + α₃·L_avg(θ) + α₄·R(θ)

Where:

• S(θ): Slope Change Penalty - Counts directional reversals in the filtered signal
• L_recent(θ): Instantaneous Lag Error - |y[n] - x[n]| at the most recent sample
• L_avg(θ): Average Tracking Error - Mean absolute error over the processing window
• R(θ): False Reversal Penalty - Trend changes that don't match the clean signal
• Weighting coefficients: α₁=0.3, α₂=α₃=0.2, α₄=0.3

Additional Evaluation Metrics

• Signal Fidelity: Correlation with ground truth clean signal
• Noise Reduction: Improvement in signal-to-noise ratio
• Computational Efficiency: Processing time per sample
• Robustness: Consistent performance across diverse signal types

Proposed Algorithmic Approaches

The initial implementation provides a foundation that evolution can improve upon:

1. Baseline Implementation

• Simple moving average filter
• Weighted exponential moving average

2. Potential Advanced Techniques (for evolution to discover)

• Adaptive Filtering: Kalman filters, particle filters, adaptive weights
• Multi-Scale Processing: Wavelet decomposition, empirical mode decomposition
• Predictive Enhancement: Local polynomial fitting, neural network prediction
• Trend Detection: Change point detection, momentum indicators
• Hybrid Approaches: Ensemble methods combining multiple techniques

File Structure

signal_processing/
├── README.md              # This documentation
├── config.yaml           # OpenEvolve configuration
├── initial_program.py     # Initial signal processing implementation
├── evaluator.py          # Multi-objective evaluation system
├── requirements.txt       # Python dependencies
└── results/              # Generated results (after running)

How to Run

Prerequisites

1. Install OpenEvolve and its dependencies
2. Install example-specific requirements:

   pip install -r requirements.txt

3. Set up your LLM API key (e.g., OPENAI_API_KEY environment variable)

Testing the Setup (Recommended)

First, validate that everything is working correctly:

cd examples/signal_processing
python test_setup.py

This will test the initial implementation and evaluator to ensure everything is ready for evolution.

Running the Evolution

From the OpenEvolve root directory:

python openevolve-run.py examples/signal_processing/config.yaml

Or from the signal_processing directory:

python ../../openevolve-run.py config.yaml

Monitoring Progress

The evolution will create an openevolve_output directory containing:

• Checkpoints: Saved population states at regular intervals
• Logs: Detailed evolution progress and metrics
• Best Programs: Top-performing algorithm implementations

Understanding the Results

Key Metrics to Watch

1. Overall Score: Primary selection metric (higher is better)
2. Composite Score: The main J(θ) optimization function
3. Correlation: How well the filtered signal matches the clean ground truth
4. Noise Reduction: Improvement in signal quality
5. Slope Changes: Number of directional reversals (lower is better)
6. Success Rate: Fraction of test signals processed successfully

Actual Evolution Patterns (Observed)

• Early iterations (1-10): Discovered Savitzky-Golay filtering with adaptive polynomial order
• Mid evolution (10-100): Parameter optimization and performance stabilization around 0.37 score
• Advanced stages (100-130): Breakthrough to full Kalman Filter implementation with state-space modeling

Test Signal Characteristics

The evaluator uses 5 different synthetic test signals to ensure robustness:

1. Smooth Sinusoidal: Basic sinusoid with linear trend
2. Multi-Frequency: Multiple frequency components combined
3. Non-Stationary: Frequency-modulated signal
4. Step Changes: Sudden level changes to test responsiveness
5. Random Walk: Stochastic process with trend

Each signal has different noise levels and lengths to test algorithm adaptability.

Initial Algorithm Analysis

The starting point includes:

• Basic moving average: Simple but may over-smooth
• Weighted moving average: Emphasizes recent samples
• Exponential weighting: Exponentially decaying weights for trend preservation

This provides a baseline that evolution can significantly improve upon by discovering:

• Advanced filtering techniques
• Adaptive parameter adjustment
• Multi-scale processing
• Predictive elements
• Robust trend detection

Interpreting Evolution Results

Successful Evolution Indicators

• Decreasing slope changes: Algorithm learns to reduce noise-induced reversals
• Improving correlation: Better preservation of true signal structure
• Balanced metrics: Good performance across all test signals
• Stable improvements: Consistent gains over multiple iterations

Common Evolution Discoveries

• Adaptive window sizing: Dynamic adjustment based on signal characteristics
• Multi-pass filtering: Combining multiple filtering stages
• Outlier detection: Identifying and handling anomalous samples
• Frequency analysis: Spectral-based filtering decisions
• Predictive elements: Using future sample prediction to reduce lag

Configuration Options

Key parameters in config.yaml:

• max_iterations: Total evolution steps (200 recommended)
• population_size: Number of candidate algorithms (80)
• cascade_thresholds: Quality gates for evaluation stages
• system_message: Guides LLM toward signal processing expertise

Extending the Example

Adding New Test Signals

Modify generate_test_signals() in evaluator.py to include:

• Real-world datasets (financial, sensor, biomedical)
• Domain-specific signal characteristics
• Different noise models and intensities

Customizing Evaluation Metrics

Adjust weights in the composite function or add new metrics:

• Phase delay measurement
• Spectral preservation
• Computational complexity analysis
• Memory usage optimization

Advanced Algorithmic Constraints

Modify the evolution block to explore:

• Specific filtering architectures
• Hardware-optimized implementations
• Online learning capabilities
• Multi-channel processing

Research Applications

This framework can be adapted for various domains:

• Financial Markets: High-frequency trading signal processing
• Biomedical Engineering: Real-time biosignal filtering
• Sensor Networks: Environmental monitoring and noise reduction
• Control Systems: Real-time feedback signal conditioning
• Communications: Adaptive signal processing for wireless systems

Actual Evolution Results ✨

After 130 iterations, OpenEvolve achieved a major algorithmic breakthrough!

Key Discoveries:

• 🎯 Full Kalman Filter Implementation: Complete state-space modeling with position-velocity tracking
• 📈 23% Performance Improvement: Composite score improved from ~0.30 to 0.3712
• ⚡ 2x Faster Execution: Optimized from 20ms to 11ms processing time
• 🔧 Advanced Parameter Tuning: Discovered optimal noise covariance matrices

Evolution Timeline:

1. Early Stage (1-10 iterations): Discovered Savitzky-Golay adaptive filtering
2. Mid Evolution (10-100): Parameter optimization and technique refinement
3. Breakthrough (100-130): Full Kalman Filter with adaptive initialization

Final Performance Metrics:

• Composite Score: 0.3712 (multi-objective optimization function)
• Slope Changes: 322.8 (19% reduction in spurious reversals)
• Correlation: 0.147 (22% improvement in signal fidelity)
• Lag Error: 0.914 (24% reduction in responsiveness delay)

Algorithmic Sophistication Achieved:

# Discovered Kalman Filter with optimized parameters:
class KalmanFilter:
    def __init__(self, sigma_a_sq=1.0, measurement_noise=0.04):
        # State transition for constant velocity model
        self.F = np.array([[1, dt], [0, 1]])
        # Optimized process noise (100x improvement)
        self.Q = G @ G.T * sigma_a_sq  
        # Tuned measurement trust (55% improvement)
        self.R = measurement_noise

The evolved solution demonstrates that automated algorithm discovery can achieve expert-level signal processing implementations, discovering sophisticated techniques like Kalman filtering and optimal parameter combinations that would typically require months of engineering effort.