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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Aug 10·5 min read
PACE: Primitive-Aware Code Evolution for Automated Algorithm Design
TL;DR
PACE introduces a novel approach to automated algorithm design by using Executable Algorithmic Primitives (EAPs) to separate local logic from complete programs. This method enhances algorithm evolution while preserving valuable code components, leading to the discovery of competitive algorithms across various tasks.
✦ Why It Matters
Consider adopting PACE to improve your algorithm design process by preserving and reusing valuable code components.
Key Takeaways
- Explore PACE for efficient algorithm design and code reuse.
- Utilize Executable Algorithmic Primitives to enhance modularity.
- Implement Thompson sampling for performance-driven evaluations.
- Test PACE on diverse tasks to validate its effectiveness.
How It Works
PACE decouples local logic from complete programs by representing it as Executable Algorithmic Primitives (EAPs). This allows for the retention and transfer of valuable code snippets across different algorithms.
The algorithm evolution process is guided by primitive-aware operators that ensure these components are structurally preserved. By using Thompson sampling, PACE evaluates the performance of these primitives based on their improvements relative to parent algorithms, streamlining the selection process without needing extra datasets.