Real-Time Adaptive Signal Processing

Evolve a real-time adaptive filtering algorithm for non-stationary time series data. The algorithm must filter noise while preserving signal dynamics and minimizing computational latency.

Problem

Input: Univariate time series with non-linear dynamics, non-stationary statistics, and rapidly changing spectral characteristics.

Constraints: Causal processing (finite sliding window), fixed latency, real-time capability.

Multi-objective function:

J(theta) = 0.3*S + 0.2*L_recent + 0.2*L_avg + 0.3*R

S: Slope change penalty (directional reversals in filtered signal)
L_recent: Instantaneous lag error
L_avg: Average tracking error
R: False reversal penalty (noise-induced trend changes)

The evaluator tests on 5 synthetic signals: sinusoidal, multi-frequency, non-stationary, step changes, and random walk.

Run

# From repo root
uv run skydiscover-run \
  benchmarks/math/signal_processing/initial_program.py \
  benchmarks/math/signal_processing/evaluator.py \
  -c benchmarks/math/signal_processing/config.yaml \
  -s [your_algorithm] \
  -i 100

Scoring

combined_score: Composite J(theta) metric (higher is better)
Also reports: slope changes, correlation, lag error, noise reduction, processing time

Files

File	Description
`initial_program.py`	Seed: basic moving average / weighted exponential filters
`evaluator.py`	Multi-objective evaluation across 5 synthetic test signals
`config.yaml`	LLM and evaluator settings
`requirements.txt`	Python dependencies