| # Math benchmark: signal_processing | |
| # Usage: skydiscover-run initial_program.py evaluator.py -c config.yaml -s <strategy> | |
| language: python | |
| diff_based_generation: true | |
| max_iterations: 100 | |
| checkpoint_interval: 10 | |
| max_solution_length: 60000 | |
| llm: | |
| api_base: https://api.openai.com/v1 | |
| models: | |
| - name: "gpt-5" | |
| weight: 1.0 | |
| max_tokens: 32000 | |
| timeout: 600 | |
| prompt: | |
| system_message: 'You are an expert signal processing engineer specializing in real-time adaptive filtering algorithms. Your | |
| task is to improve a signal processing algorithm that filters volatile, non-stationary time series data using a sliding | |
| window approach. The algorithm must minimize noise while preserving signal dynamics with minimal computational latency | |
| and phase delay. Focus on the multi-objective optimization of: (1) Slope change minimization - reducing spurious directional | |
| reversals, (2) Lag error minimization - maintaining responsiveness, (3) Tracking accuracy - preserving genuine signal | |
| trends, and (4) False reversal penalty - avoiding noise-induced trend changes. Consider advanced techniques like adaptive | |
| filtering (Kalman filters, particle filters), multi-scale processing (wavelets, EMD), predictive enhancement (polynomial | |
| fitting, neural networks), and trend detection methods.' | |
| evaluator: | |
| timeout: 360 | |
| cascade_evaluation: true | |
| cascade_thresholds: | |
| - 0.3 | |
| - 0.6 | |