# Automated Design of Metaheuristic Algorithms: A Survey

arXiv: https://arxiv.org/abs/2303.06532 · HTML: https://mlsanity.com/paper/2303.06532/

## 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 (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Designing Network Design Spaces](https://mlsanity.com/paper/2003.13678/) (2020)
- [Transfer Learning in Deep Reinforcement Learning: A Survey](https://mlsanity.com/paper/2009.07888/) (2020)
- [SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization](https://mlsanity.com/paper/2109.09831/) (2021)
- [Automated Algorithm Selection: Survey and Perspectives](https://mlsanity.com/paper/1811.11597/) (2018)
- [ParamILS: An Automatic Algorithm Configuration Framework](https://mlsanity.com/paper/1401.3492/) (2014)
- [Discovering Evolution Strategies via Meta-Black-Box Optimization](https://mlsanity.com/paper/2211.11260/) (2022)
- [Searching the Search Space of Vision Transformer](https://mlsanity.com/paper/2111.14725/) (2021)
- [Deep Reinforcement Learning Based Parameter Control in Differential Evolution](https://mlsanity.com/paper/1905.08006/) (2019)
- [A Survey of Methods for Automated Algorithm Configuration](https://mlsanity.com/paper/2202.01651/) (2022)
- [Learning adaptive differential evolution algorithm from optimization experiences by policy gradient](https://mlsanity.com/paper/2102.03572/) (2021)
- [Evolving Search Space for Neural Architecture Search](https://mlsanity.com/paper/2011.10904/) (2020)
- [Evolving the Structure of Evolution Strategies](https://mlsanity.com/paper/1610.05231/) (2016)
- [Automated Configuration of Genetic Algorithms by Tuning for Anytime Performance](https://mlsanity.com/paper/2106.06304/) (2021)
- [Evolving Evolutionary Algorithms using Linear Genetic Programming](https://mlsanity.com/paper/2109.13110/) (2021)
- [Evolutionary Diversity Optimisation for The Traveling Thief Problem](https://mlsanity.com/paper/2204.02709/) (2022)
- [Paradiseo: From a Modular Framework for Evolutionary Computation to the Automated Design of Metaheuristics ---22 Years of Paradiseo---](https://mlsanity.com/paper/2105.00420/) (2021)
- [Principled Design of Translation, Scale, and Rotation Invariant Variation Operators for Metaheuristics](https://mlsanity.com/paper/2105.10657/) (2021)
- [AutoOpt: A General Framework for Automatically Designing Metaheuristic Optimization Algorithms with Diverse Structures](https://mlsanity.com/paper/2204.00998/) (2022)
- [Analyzing the Impact of Undersampling on the Benchmarking and Configuration of Evolutionary Algorithms](https://mlsanity.com/paper/2204.09353/) (2022)
- [Evolving Continuous Optimisers from Scratch](https://mlsanity.com/paper/2103.11746/) (2021)
- [Instruction-Level Design of Local Optimisers using Push GP](https://mlsanity.com/paper/1905.10245/) (2019)
- [Towards Large Scale Automated Algorithm Design by Integrating Modular Benchmarking Frameworks](https://mlsanity.com/paper/2102.06435/) (2021)

## Built on by (most cited first, 12 of 12)

- [A Systematic Survey on Large Language Models for Algorithm Design](https://mlsanity.com/paper/2410.14716/) (2024)
- [Toward Automated Algorithm Design: A Survey and Practical Guide to Meta-Black-Box-Optimization](https://mlsanity.com/paper/2411.00625/) (2024)
- [Deep Insights into Automated Optimization with Large Language Models and Evolutionary Algorithms](https://mlsanity.com/paper/2410.20848/) (2024)
- [Automated Metaheuristic Algorithm Design with Autoregressive Learning](https://mlsanity.com/paper/2405.03419/) (2024)
- [RedAHD: Reduction-Based End-to-End Automatic Heuristic Design with Large Language Models](https://mlsanity.com/paper/2505.20242/) (2025)
- [From Performance to Understanding: A Vision for Explainable Automated Algorithm Design](https://mlsanity.com/paper/2511.16201/) (2025)
- [CogMCTS: A Novel Cognitive-Guided Monte Carlo Tree Search Framework for Iterative Heuristic Evolution with Large Language Models](https://mlsanity.com/paper/2512.08609/) (2025)
- [READY: Reward Discovery for Meta-Black-Box Optimization](https://mlsanity.com/paper/2601.21847/) (2026)
- [CliffSearch: Structured Agentic Co-Evolution over Theory and Code for Scientific Algorithm Discovery](https://mlsanity.com/paper/2604.01210/) (2026)
- [Teacher-Aware Evolution of Heuristic Programs from Learned Optimization Policies](https://mlsanity.com/paper/2605.10634/) (2026)
- [SCOPE: Synthetic Conditional Objectives for Policy Evolution in Black-Box Combinatorial Optimization](https://mlsanity.com/paper/2607.27630/) (2026)
- [Hyper Algorithm Design Agent: Evolving Learnable Optimizer from Zero](https://mlsanity.com/paper/2609.35328/) (2026)

## 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.