mlsanityData to 14 Oct 2026

From Hand-Crafted to LLM-Based Variation Operators in Metaheuristics: A Tutorial

arXiv 2609.31649 ↗First posted 13 Sep 2026cs.NE cs.AI cs.CLcited by 0

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Abstract (arXiv)

Large language models (LLMs) are increasingly being employed as variation operators in metaheuristics, generating or modifying candidate solutions, heuristics, or programs inside iterative search loops. This shift reframes variation as a model call conditioned on different types of information. We introduce an operator-level framework with two descriptors: (1) the type of prompt-conditioning information at variation time (textttNumeric, textttSymbolic, textttLinguistic), and (2) artifact persistence, identifying what survives the model call (textttTransient, textttAmortized, textttTransfer). The tutorial shows how to classify, build, and select these operators through a worked build template, a method survey, an evidence table, and a cost-aware decision guide.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 63 of its 118 references that are in the map. First posted on arXiv 13 Sep 2026.

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63 of 118 references.

References · 63

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38 more
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