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ReEvo: Large Language Models as Hyper-Heuristics with Reflective Evolution

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arxiv 2402.01145 v3 pith:TBOL4GMW submitted 2024-02-02 cs.NE cs.AI

Haoran Ye , Jiarui Wang , Zhiguang Cao , Federico Berto , Chuanbo Hua , Haeyeon Kim , Jinkyoo Park , Guojie Song This is my paper
classification cs.NEcs.AI
keywords heuristiccopshyper-heuristicslanguagelhhsreevodesignevolution
verification ladder T0 review T1 audit T2 compute T3 formal
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The omnipresence of NP-hard combinatorial optimization problems (COPs) compels domain experts to engage in trial-and-error heuristic design. The long-standing endeavor of design automation has gained new momentum with the rise of large language models (LLMs). This paper introduces Language Hyper-Heuristics (LHHs), an emerging variant of Hyper-Heuristics that leverages LLMs for heuristic generation, featuring minimal manual intervention and open-ended heuristic spaces. To empower LHHs, we present Reflective Evolution (ReEvo), a novel integration of evolutionary search for efficiently exploring the heuristic space, and LLM reflections to provide verbal gradients within the space. Across five heterogeneous algorithmic types, six different COPs, and both white-box and black-box views of COPs, ReEvo yields state-of-the-art and competitive meta-heuristics, evolutionary algorithms, heuristics, and neural solvers, while being more sample-efficient than prior LHHs.

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Cited by 11 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. AutoSND: From Execution Evidence to Structural Policies for Automated Network Dismantling Heuristic Discovery

    cs.AI 2026-08 conditional novelty 7.0 of 10

    A three-stage LLM search framework converts execution evidence into structural policies and discovers network dismantling programs with strong quality and low runtime.

  2. EvoGit: Decentralized Code Evolution via Git-Based Multi-Agent Collaboration

    cs.DC 2025-06 conditional novelty 7.0 of 10

    A Git-based phylogenetic graph lets independent AI agents collaboratively evolve code through mutation and crossover, demonstrated on a website and a meta-level bin-packing solver.

  3. Similarity-Guided Curriculum Fine-Tuning of LLMs for Neural Architecture Synthesis

    cs.CV 2026-07 conditional novelty 6.0 of 10

    MinHash-band curriculum fine-tuning of OlympicCoder-7B yields 60% peak success on high-similarity NAS code generation, yet merge drift and interface repair dominate later stages.

  4. iDSE: Navigating Design Space Exploration in High-Level Synthesis Using LLMs

    cs.AR 2025-05 conditional novelty 6.0 of 10

    An LLM-based design space exploration system for HLS combines design-space pruning, LLM-generated seed directives, and convergent/divergent refinement to approximate Pareto-optimal designs with few synthesis evaluations.

  5. Graph Optimization Foundation Model: Tokenizing Graph via A Language-Model Paradigm

    cs.LG 2025-09 reject novelty 5.0 of 10

    A per-graph BERT-style masked random-walk model is repurposed to generate shortest paths and tours, with mixed quality versus classical solvers and no cross-graph transfer evaluation.

  6. Behaviour Space Analysis of LLM-driven Meta-heuristic Discovery

    cs.NE 2025-07 conditional novelty 5.0 of 10

    Comparing six LLaMEA prompt and selection variants on 5D BBOB problems, the 1+1 elitist variant using both simplify and random-perturbation prompts produced the best anytime performance, and behaviour metrics link thi...

  7. REMoH: A Reflective Evolution of Multi-objective Heuristics approach via Large Language Models

    cs.AI 2025-06 reject novelty 5.0 of 10

    REMoH evolves LLM-written heuristics with NSGA-II and a reflection mechanism, reporting competitive FJSSP results that are weakened by test-set selection.

  8. Enhancing CVRP Solver through LLM-driven Automatic Heuristic Design

    cs.AI 2026-02 conditional novelty 4.0 of 10

    LLM-evolved ruin heuristics, embedded in an iterated local search solver, produce 8 new best-known solutions on large-scale CVRPLib instances and a lower average gap than HGS and AILS-II.

  9. BuildEvo: Designing Building Energy Consumption Forecasting Heuristics via LLM-driven Evolution

    cs.AI 2025-07 reject novelty 4.0 of 10

    BuildEvo evolves interpretable Python heuristics for building energy forecasting via LLM-driven search, reporting lower error on five BDG2 buildings than the baselines tested.

  10. STRCMP: Integrating Graph Structural Priors with Language Models for Combinatorial Optimization

    cs.LG 2025-05 reject novelty 4.0 of 10

    STRCMP's GNN-plus-LLM code search for MILP and SAT heuristics does not consistently beat AutoSAT in the paper's own reported numbers.

  11. Evolutionary Computation and Large Language Models: A Survey of Methods, Synergies, and Applications

    cs.NE 2025-05 conditional novelty 4.0 of 10

    A survey that maps bidirectional synergies between evolutionary computation and large language models and proposes a taxonomy plus research gaps.

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