mlsanityData to 14 Oct 2026

Automated Design of Metaheuristic Algorithms: A Survey

arXiv 2303.06532 ↗First posted 12 Mar 2023cs.NE cs.LGcited by 12

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Abstract (arXiv)

Metaheuristics have gained great success in academia and practice because their search logic can be applied to any problem with available solution representation, solution quality evaluation, and certain notions of locality. Manually designing metaheuristic algorithms for solving a target problem is criticized for being laborious, error-prone, and requiring intensive specialized knowledge. This gives rise to increasing interest in automated design of metaheuristic algorithms. With computing power to fully explore potential design choices, the automated design could reach and even surpass human-level design and could make high-performance algorithms accessible to a much wider range of researchers and practitioners. This paper presents a broad picture of automated design of metaheuristic algorithms, by conducting a survey on the common grounds and representative techniques in terms of design space, design strategies, performance evaluation strategies, and target problems in this field.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 24 of its 157 references that are in the map. First posted on arXiv 12 Mar 2023.

Builds on

24 of 157 references.

References · 24

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12 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 4; 2025: 3; 2026: 5.

12 papers.

  1. 2024A Systematic Survey on Large Language Models for Algorithm Designcited by 69
  2. 2024Toward Automated Algorithm Design: A Survey and Practical Guide to Meta-Black-Box-Optimizationcited by 24
  3. 2024Deep Insights into Automated Optimization with Large Language Models and Evolutionary Algorithmscited by 12
  4. 2024Automated Metaheuristic Algorithm Design with Autoregressive Learningcited by 7
  5. 2025RedAHD: Reduction-Based End-to-End Automatic Heuristic Design with Large Language Modelscited by 4
  6. 2025From Performance to Understanding: A Vision for Explainable Automated Algorithm Designcited by 0
  7. 2025CogMCTS: A Novel Cognitive-Guided Monte Carlo Tree Search Framework for Iterative Heuristic Evolution with Large Language Modelscited by 0
  8. 2026READY: Reward Discovery for Meta-Black-Box Optimizationcited by 0
  9. 2026CliffSearch: Structured Agentic Co-Evolution over Theory and Code for Scientific Algorithm Discoverycited by 0
  10. 2026Teacher-Aware Evolution of Heuristic Programs from Learned Optimization Policiescited by 0
  11. 2026SCOPE: Synthetic Conditional Objectives for Policy Evolution in Black-Box Combinatorial Optimizationcited by 0
  12. 2026Hyper Algorithm Design Agent: Evolving Learnable Optimizer from Zerocited by 0