Latent Heuristic Search: Continuous Optimization for Automated Algorithm Design
The paper presents a new framework for automated algorithm design called Latent Heuristic Search, which utilizes continuous optimization. By integrating Large Language Models into evolutionary frameworks, the authors propose a method that maps discrete programs into continuous embeddings for improved performance prediction. Empirical evaluations show that this approach competes well with existing discrete evolutionary methods across several optimization problems.
- ▪The proposed framework shifts optimization to a learned latent manifold.
- ▪An encoder maps discrete programs into continuous embeddings for gradient-based search.
- ▪The method was evaluated on problems like the Traveling Salesman Problem and achieved competitive performance.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.17137 |
| Publication time | Tue, 19 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-19T04:04:57.272Z |
| Last seen | 2026-05-19T04:04:57.272Z |
| Headline source | Publisher (no WeSearch rewrite) |
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| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | S5eT4Yjlc6BO |
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| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Artificial Intelligence arXiv:2605.17137 (cs) [Submitted on 16 May 2026] Title:Latent Heuristic Search: Continuous Optimization for Automated Algorithm Design Authors:Cheikh Ahmed, Mahdi Mostajabdaveh, Zirui Zhou View a PDF of the paper titled Latent Heuristic Search: Continuous Optimization for Automated Algorithm Design, by Cheikh Ahmed and 2 other authors View PDF HTML (experimental) Abstract:The integration of Large Language Models (LLMs) into evolutionary frameworks has established a new paradigm for automated heuristic discovery. Despite their promise, these methods typically search in the discrete space of program syntax, relying on stochastic sampling to navigate a highly non-convex optimization landscape.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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