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.
arXiv cs.AI files mainly under ai research. We currently carry 1,128 of its stories.
Opening excerpt (first ~120 words) tap to expand
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.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
Discussion
0 commentsMore from arXiv cs.AI
