Multi-objective Evolution of Heuristic Using Large Language Model
Source
- Person key:
ysymyth - Source kind:
paper - Canonical URL: https://arxiv.org/abs/2409.16867
- License:
NOASSERTION - Public handling:
public-metadata-summary-hash-link-only - Semantic hash:
7665376b1d570898967ddfc49b88b27bcd675806b1ac6e7b8d75aa7ffa07c9aa - First seen: 2026-05-16
- Last changed: 2026-05-16
- Identity guard: Do not confuse with yao-shunyu-alfred, the physics-to-AI researcher at alfredyao.github.io.
Classification
- Category: Software engineering agents and benchmarks
- Topic hub: shunyu-yao-public-corpora
- Project taxonomy: shunyu-yao-project-taxonomy
- Paper map: shunyu-yao-paper-map
Summary
Heuristics are commonly used to tackle various search and optimization problems. Design heuristics usually require tedious manual crafting with domain knowledge. Recent works have incorporated Large Language Models (LLMs) into automatic heuristic search, leveraging their powerful language and coding capacity. However, existing research focuses on the optimal performance on the target problem as the sole objective, neglecting other criteria such as efficiency and scalability, which are vital in practice. To tackle t…
What This Teaches
How coding agents are benchmarked, scaffolded, and constrained against real repositories and issues.
Related
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