Evolutionary Multi-Task Optimization for LLM-Guided Program Discovery
Recent LLM-guided evolutionary search methods have shown that iterative program mutation can discover strong algorithms, but they typically optimize each task independently, even when related tasks share reusable structure. We study evolutionary multitask optimization (EMO) for LLM-guided program discovery and propose EMO-STA (Shared-Then-Adapt), a two-stage framework that first evolves a shared archive of executable programs across a task family and then adapts selected shared candidates to each target task. Within EMO-STA, we explore multiple adaptation strategies, including warm-starting from the shared archive, adapting the best average shared program, and adapting the shared program ...
| Authors | Halil Alperen Gozeten, Xuechen Zhang, Emrullah Ildiz et al. |
|---|---|
| Primary category | cs.LG |
| Published | 21 May 2026 |
| Last updated at source | 14 October 2026 |
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