📦️This is a collection of MetaBBO papers and their corresponding code resources (👈️Click here for the paper list).
Black Box Optimization (BBO) refers to a class of optimization problems where the objective function is defined as $f: X \rightarrow \mathbb{R}$. The term "Black-Box" means that, although we can evaluate $f(X)$ for any $X$ within the search domain, we have no access to additional information such as the mathematical expression, gradients, or any structural details. The only available data comes from the input $X$ and the corresponding output $f(X)$. BBO problems can be categorized into SOP(singe-objective), MOOP(multi-objective), COP(constrained), CMOP(constrained multi-objective), MMOP(multi-modal), MMOOP(multi-modal multi-objective), LSOP(large-scale), LS-MOOP(large-scale multi-objective), CO(combinatorial) optimization problem and MILP(mixed integer linear programming) based on their specific characteristics.
🔥Meta-Black-Box-Optimization (MetaBBO) is an emerging research topic, leveraging the generalization power of Meta Learning to enhance the performance of existing BBO optimizers, or create new ones. 🚀By utilizing Meta Learning, the reliance on expert-level knowledge is reduced, highlighting the trend toward automated algorithm design in BBO.
🚩We warmly invite you to read our survey on MetaBBO, "Toward Automated Algorithm Design: A Survey and Practical Guide to Meta-Black-Box-Optimization," for more detailed insights into MetaBBO! Besides, if you find this repository useful, please cite it in your publications or projects as follows.
@article{ma2024toward,
title={Toward Automated Algorithm Design: A Survey and Practical Guide to Meta-Black-Box-Optimization},
author={Ma, Zeyuan and Guo, Hongshu and Gong, Yue-Jiao and Zhang, Jun and Tan, Kay Chen},
journal={IEEE Transactions on Evolutionary Computation},
year={2025}
}
👨💻👩💻We are a research team mainly focus on Meta-Black-Box-Optimization (MetaBBO), which assists automated algorithm design for Evolutionary Computation.
Here is our homepage and github. 🥰🥰🥰Please feel free to contact us—any suggestions are welcome!
✨️MetaBBO is rapidly evolving, and this is by no means a comprehensive list of papers, which would be continuously matained and updated.
If you want to update the list or have any question：
😘Join us in perfecting the MetaBBO papers and their code resources together!
👍️👍️👍️Many outstanding teams have developed excellent GitHub repositories for the Evolutionary Computation community, and we are pleased to share them here.
| Repository | About |
|---|---|
| MetaBox | MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning |
| LLM4Opt | A Collection on Large Language Models for Optimization |
| pypop7 | A Pure-Python Library for POPulation-based Black-Box Optimization |
| EvoX | Distributed GPU-Accelerated Framework for Evolutionary Computation. Comprehensive Library of Evolutionary Algorithms & Benchmark Problems |
| evosax | Evolution Strategies in JAX |
We first classify them by learning paradigm, followed by different automated algorithm design tasks.
All papers are sorted by year of publication.
*The complete list of IEEE CEC series can be access at ntu.edu.sg.
*The complete list of BBOB series can be access at numbbo.
| Algorithm | Paper | Optimization Type | Low-Level Optimizer | RL | Code Resource |
|---|---|---|---|---|---|
| TSRL-CMM | Liu X, Wang T, Zeng Z, et al. "Three stage based reinforcement learning for combining multiple metaheuristic algorithms". Swarm and Evolutionary Computation, 2025, 95: 101935. | SOP | EAs | Tabular Q-learning | TSRL-CMM |
| HHRL-MAR | Zhu N, Zhao F, Cao J. "A Hyperheuristic and Reinforcement Learning Guided Meta-heuristic Algorithm Recommendation" 2024 27th International Conference on Computer Supported Cooperative Work in Design (2024) | SOP | SI | Tabular Q-learning | - |
| R2-RLMOEA | Tahernezhad-Javazm F, Rankin D, Bois N D, et al. "R2 Indicator and Deep Reinforcement Learning Enhanced Adaptive Multi-Objective Evolutionary Algorithm". arXiv preprint arXiv:2404.08161 (2024). | MOOP | EAs | DDQN | - |
| RL-DAS | Guo, Hongshu, et al. "Deep Reinforcement Learning for Dynamic Algorithm Selection: A Proof-of-Principle Study on Differential Evolution." IEEE Transactions on Systems, Man, and Cybernetics: Systems (2024). | SOP | DE | PPO | RL-DAS |
| Algorithm | Paper | Optimization Type | Low-Level Optimizer | RL | Code Resource |
|---|---|---|---|---|---|
| MSDE | Lu, Yiling, et al. "A multi-strategy self-adaptive differential evolution algorithm for assembly hybrid flowshop lot-streaming scheduling with component sharing." Swarm and Evolutionary Computation 92 (2025). | SOP | DE | Tabular Q-learning | - |
| rlDE | Yang, Xu, et al. "Reinforcement learning Based Automated Design of Differential Evolution Algorithm for Black-box Optimization." arXiv preprint arXiv:2501.12881 (2025). | SOP | DE | DDQN | - |
| DRL-SAEA | Shao, Shuai, Ye Tian, and Yajie Zhang. "Deep reinforcement learning assisted surrogate model management for expensive constrained multi-objective optimization." Swarm and Evolutionary Computation 92 (2025). | ECMOP | SAEA | DQN | - |
| ConfigX | Guo, Hongshu, et al. "ConfigX: Modular Configuration for Evolutionary Algorithms via Multitask Reinforcement Learning." arXiv preprint arXiv:2412.07507 (2024). | SOP | EAs | PPO | - |
| RL_DNSABC | Ye, Tingyu, et al. "Reinforcement learning-driven dual neighborhood structure artificial bee colony algorithm for continuous optimization problem." Applied Soft Computing (2024) | SOP | ABC | Tabular Q-learning | - |
| QLBOS2N | Cheng, Shi, et al. "A Q-Learning Based Brainstorming Optimization Algorithm for Solving Multimodal Optimization Problems." IEEE Transactions on Consumer Electronics (2024). | MMOPs | BSO | Tabular Q-learning | - |
| AuDE | Cao, Zijian, et al. "An autonomous differential evolution based on reinforcement learning for cooperative countermeasures of unmanned aerial vehicles." Applied Soft Computing (2024). | SOP | DE | Tabular Q-learning | - |
| DRLCEA | Luo, Wenguan, et al. "Deep reinforcement learning-guided coevolutionary algorithm for constrained multiobjective optimization." Information Sciences (2024). | CMOP | EAs | DQN | - |
| HF | Pei J, Liu J, Mei Y. "Learning from Offline and Online Experiences: A Hybrid Adaptive Operator Selection Framework" Proceedings of the Genetic and Evolutionary Computation Conference. (2024). | SOP,CO | DE | DDQN | - |
| UES-CMAES-RL | Bolufé-Röhler A, Xu B. "Deep Reinforcement Learning for Smart Restarts in Exploration-Only Exploitation-Only Hybrid Metaheuristics Metaheuristics International Conference" (2024). | SOP | UES-CMAES | DQN | - |
| MTDE-L2T | Wu S H, Huang Y, Wu X, et al. "Learning to Transfer for Evolutionary Multitasking". arXiv preprint arXiv:2406.14359, (2024). | MTOP | EC | PPO | - |
| MSoRL | Wang X, Wang F, He Q, et al. "A multi-swarm optimizer with a reinforcement learning mechanism for large-scale optimization". Swarm and Evolutionary Computation (2024). | LSOP | PSO | Tabular Q-learning | - |
| MRL-MOEA | Wang, Jing, et al. "A Novel Multi-State Reinforcement Learning-Based Multi-Objective Evolutionary Algorithm." Information Sciences (2024). | MOOP | MOEA | Tabular Q-learning | - |
| RLEMMO | Lian, Hongqiao, et al. "RLEMMO: Evolutionary Multimodal Optimization Assisted By Deep Reinforcement Learning." Proceedings of the Genetic and Evolutionary Computation Conference (2024). | MMOP | DE | PPO | - |
| SA-DQN-DE | Liao, Zuowen, Qishuo Pang, and Qiong Gu. "Differential evolution based on strategy adaptation and deep reinforcement learning for multimodal optimization problems." Swarm and Evolutionary Computation 87 (2024): 101568. | MMOP | DE | DQN | - |
| PG-DE & PG-MPEDE | Zhang, Haotian, et al. "Learning to select the recombination operator for derivative-free optimization." Science China Mathematics (2024). | SOP | DE | REINFORCE | - |
| RLNS | Hong, Jiale, Bo Shen, and Anqi Pan. "A reinforcement learning-based neighborhood search operator for multi-modal optimization and its applications." Expert Systems with Applications (2024). | MMOP | SSA,PSO,EO | Tabular Q-learning | - |
| RLMODE | Yu, Xiaobing, et al. "Reinforcement learning-based differential evolution algorithm for constrained multi-objective optimization problems." Engineering Applications of Artificial Intelligence (2024). | MOOP | DE | Tabular Q-learning | - |
| GLEET | Ma, Zeyuan, et al. "Auto-configuring Exploration-Exploitation Tradeoff in Evolutionary Computation via Deep Reinforcement Learning." Proceedings of the Genetic and Evolutionary Computation Conference (2024). | SOP | DE,PSO | PPO | GLEET |
| RLMFEA | Li, Shuijia, et al. "Evolutionary multitasking via reinforcement learning." IEEE Transactions on Emerging Topics in Computational Intelligence 8.1 (2023). | MFO | EAs | Tabular Q-learning | - |
| RLHDE | Peng L, Yuan Z, Dai G, et al. "Reinforcement learning-based hybrid differential evolution for global optimization of interplanetary trajectory design". Swarm and Evolutionary Computation, (2023). | SOP | HLSHADE | Tabular Q-learning | - |
| AMODE-DRL | Li T, Meng Y, Tang L. "Scheduling of continuous annealing with a multi-objective differential evolution algorithm based on deep reinforcement learning". IEEE Transactions on Automation Science and Engineering (2023). | MOOP | MODE | DDQN+DDPG | - |
| MARLABC | Zhao F, Wang Z, Wang L, et al. "A multi-agent reinforcement learning driven artificial bee colony algorithm with the central controller". Expert Systems with Applications (2023). | SOP | ABC | Tabular Q-learning | - |
| CEDE-DRL | Hu Z, Gong W, Pedrycz W, et al. "Deep reinforcement learning assisted co-evolutionary differential evolution for constrained optimization". Swarm and Evolutionary Computation (2023). | SOP | CO-DE | DQN | - |
| RLDMDE | Yang, Qingyong, et al. "Dynamic multi-strategy integrated differential evolution algorithm based on reinforcement learning for optimization problems." Complex & Intelligent Systems (2023). | SOP | DE | Tabular Q-learning | - |
| RLMMDE | Han Y, Peng H, Mei C, et al. "Multi-strategy multi-objective differential evolutionary algorithm with reinforcement learning". Knowledge-Based Systems (2023). | MOOP | MOEA | Tabular Q-learning | - |
| MPSORL | Meng, Xiaoding, Hecheng Li, and Anshan Chen. "Multi-strategy self-learning particle swarm optimization algorithm based on reinforcement learning." Mathematical Biosciences and Engineering (2023). | SOP | PSO | Tabular Q-learning | - |
| IRLMFO | Zhao F, Wang Q, Wang L. "An inverse reinforcement learning framework with the Q-learning mechanism for the metaheuristic algorithm". Knowledge-Based Systems (2023). | SOP | MFO | IRL+Tabual Q-learning | - |
| RLAM | Yin, Shiyuan, et al. "Reinforcement-learning-based parameter adaptation method for particle swarm optimization." Complex & Intelligent Systems (2023). | SOP | PSO | DDPG | - |
| LADE | Liu X, Sun J, Zhang Q, et al. "Learning to learn evolutionary algorithm: A learnable differential evolution". IEEE Transactions on Emerging Topics in Computational Intelligence (2023). | SOP | DE | REINFORCE | - |
| MOEADRL | Gao, Mengqi, et al. "An efficient evolutionary algorithm based on deep reinforcement learning for large-scale sparse multiobjective optimization." Applied Intelligence (2023). | LS-MOOP | SpareEAs | A2C | - |
| Q-LSHADE | Zhang H, Sun J, Bäck T, et al. "Controlling Sequential Hybrid Evolutionary Algorithm by Q-Learning". IEEE Computational Intelligence Magazine (2023). | SOP | LSHADE | Tabular Q-learning | - |
| NRLPSO | Li, Wei, et al. "Reinforcement learning-based particle swarm optimization with neighborhood differential mutation strategy." Swarm and Evolutionary Computation (2023). | SOP | PSO | Tabular Q-learning | - |
| RL-SHADE | Fister I, Fister D, Fister Jr I. "Reinforcement learning-based differential evolution for global optimization Differential Evolution: From Theory to Practice" (2022). | SOP | SHADE | Tabular Q-learning | - |
| RL-HPSDE | Tan, Zhiping, et al. "Differential evolution with hybrid parameters and mutation strategies based on reinforcement learning." Swarm and Evolutionary Computation (2022): 101194. | SOP | DE | Tabular Q-learning | - |
| MOEA/D-DQN | Tian, Ye, et al. "Deep reinforcement learning based adaptive operator selection for evolutionary multi-objective optimization." IEEE Transactions on Emerging Topics in Computational Intelligence (2022). | MOOP | MOEA | DDQN | - |
| RL-CORCO | Hu Z, Gong W. "Constrained evolutionary optimization based on reinforcement learning using the objective function and constraints". Knowledge-Based Systems (2022). | COP | DE | Tabular Q-learning | - |
| MADAC | Xue, Ke, et al. "Multi-agent dynamic algorithm configuration." Advances in Neural Information Processing Systems (2022). | MOOP | MOEA/D | VDN | - |
| RLLPSO | Wang F, Wang X, Sun S. "A reinforcement learning level-based particle swarm optimization algorithm for large-scale optimization." Information Sciences (2022). | LSOP | PSO | Tabular Q-learning | - |
| RL-PSO | Wu, Di, and G. Gary Wang. "Employing reinforcement learning to enhance particle swarm optimization methods." Engineering Optimization (2022). | SOP | PSO | REINFORCE | - |
| RLEA-SSC | Xia H, Li C, Zeng S, et al. "A reinforcement-learning-based evolutionary algorithm using solution space clustering for multimodal optimization problems 2021 IEEE Congress on Evolutionary Computation (CEC). (2021). | MMOP | DE | Tabular Q-learning | - |
| DE-DQN | Tan, Zhiping, and Kangshun Li. "Differential evolution with mixed mutation strategy based on deep reinforcement learning." Applied Soft Computing (2021). | SOP | DE | Tabular Q-learning | - |
| LDE | Sun, Jianyong, et al. "Learning Adaptive Differential Evolution Algorithm from Optimization Experiences by Policy Gradient." IEEE Transactions on Evolutionary Computation (2021). | SOP | DE | REINFORCE | yierh/LDE |
| RLEPSO | Yin, Shiyuan, et al. "RLEPSO: Reinforcement learning based Ensemble particle swarm optimizer." Proceedings of the 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence. (2021). | SOP | PSO | DDPG | - |
| RLDE | Hu Z, Gong W, Li S. "Reinforcement learning-based differential evolution for parameters extraction of photovoltaic models." Energy Reports (2021). | SOP | DE | Tabular Q-learning | - |
| LRMODE | Huang Y, Li W, Tian F, et al. "A fitness landscape ruggedness multiobjective differential evolution algorithm with a reinforcement learning strategy". Applied Soft Computing (2020). | MOOP | DE | Tabular Q-learning | - |
| MARLwCMA | Sallam, Karam M., et al. "Evolutionary framework with reinforcement learning-based mutation adaptation." IEEE Access (2020). | SOP | DE | Tabular Q-learning | - |
| QLPSO | Xu Y, Pi D. "A reinforcement learning-based communication topology in particle swarm optimization." Neural Computing and Applications (2020). | SOP | PSO | Tabular Q-learning | - |
| LTO | Shala G, Biedenkapp A, Awad N, et al. "Learning step-size adaptation in CMA-ES." Parallel Problem Solving from Nature–PPSN XVI: 16th International Conference (2020). | SOP | CMA-ES | GPS | - |
| DE-DDQN | Sharma, Mudita, et al. "Deep reinforcement learning based parameter control in differential evolution." Proceedings of the Genetic and Evolutionary Computation Conference (2019). | SOP | DE | Tabular Q-learning | mudita11/DE-DDQN |
| QL-M/S-OPSO | Liu Y, Lu H, Cheng S, et al. "An adaptive online parameter control algorithm for particle swarm optimization based on reinforcement learning" 2019 IEEE congress on evolutionary computation (2019). | SOP,MOOP | PSO | Tabular Q-learning | - |
| DE-RLFR | Li, Zhihui, et al. "Differential evolution based on reinforcement learning with fitness ranking for solving multimodal multiobjective problems" Swarm and Evolutionary Computation (2019). | MMOOP | DE | Tabular Q-learning | - |
| RL-MOEA/D | Ning W, Guo B, Guo X, et al. "Reinforcement learning aided parameter control in multi-objective evolutionary algorithm based on decomposition". Progress in Artificial Intelligence (2018). | MOOP | MOEA/D | SARSA | - |
| QFA | Sadhu A K, Konar A, Bhattacharjee T, et al. "Synergism of firefly algorithm and Q-learning for robot arm path planning". Swarm and Evolutionary Computation (2018). | SOP | FA | Tabular Q-learning | - |
| RLMPSO | Samma H, Lim C P, Saleh J M. "A new reinforcement learning-based memetic particle swarm optimizer". Applied Soft Computing (2016). | SOP | PSO | Tabular Q-learning | - |
| Algorithm | Paper | Optimization Type | Low-Level Optimizer | RL | Code Resource |
|---|---|---|---|---|---|
| ALDes | Zhao, Qi, et al. "Automated Metaheuristic Algorithm Design with Autoregressive Learning." arXiv preprint arXiv:2405.03419 (2024). | SOP | - | - | - |
| SYMBOL | Chen, Jiacheng, et al. "Symbol: Generating Flexible Black-Box Optimizers through Symbolic Equation Learning." The Twelfth International Conference on Learning Representations. (2024). | SOP | - | PPO | SYMBOL |
| GSF | Yi, Wenjie, et al. "Automated design of metaheuristics using reinforcement learning within a novel general search framework." IEEE Transactions on Evolutionary Computation (2022) | CO | - | PPO\DQN | - |
| Algorithm | Paper | Optimization Type | Low-Level Optimizer | RL | Code Resource |
|---|---|---|---|---|---|
| MELBA | Chaybouti, Sofian, et al. "Meta-learning of Black-box Solvers Using Deep Reinforcement Learning." NeurIPS 2022, MetaLearn Workshop. (2022). | SOP | - | PPO | - |
| Algorithm | Paper | Optimization Type | Low-Level Optimizer | Code Resource |
|---|---|---|---|---|
| TransOptAS | Cenikj G, Petelin G, Eftimov T. "TransOptAS: Transformer-Based Algorithm Selection for Single-Objective Optimization" Proceedings of the Genetic and Evolutionary Computation Conference Companion (2024). | SOP | EAs,SI | - |
| ASF-ALLFV | Li Y, Liang J, Yu K, et al. "Adaptive local landscape feature vector for problem classification and algorithm selection". Applied Soft Computing, (2022). | SOP | EAs,SI | - |
| AR-BB | Tian Y, Peng S, Zhang X, et al. "A recommender system for metaheuristic algorithms for continuous optimization based on deep recurrent neural networks". IEEE transactions on artificial intelligence (2020). | SOP | EAs,SI | - |
| Meta-VRP | Gutierrez-Rodríguez A E, Conant-Pablos S E, Ortiz-Bayliss J C, et al. "Selecting meta-heuristics for solving vehicle routing problems with time windows via meta-learning". Expert Systems with Applications (2019). | CO | MOEA | - |
| Meta-MOP | Tian Y, Peng S, Rodemann T, et al. "Automated selection of evolutionary multi-objective optimization algorithms" 2019 IEEE Symposium Series on Computational Intelligence. (2019). | MOOP | MOEA | - |
| Meta-TSP | Kanda J Y, de Carvalho A C, Hruschka E R, et al. "Using meta-learning to recommend meta-heuristics for the traveling salesman problem" 2011 10th international conference on machine learning and applications and workshops. (2011). | CO | GA | - |
| Meta-QAP | Smith-Miles K A. "Towards insightful algorithm selection for optimisation using meta-learning concepts" 2008 IEEE international joint conference on neural networks. (2008). | CO | MMAS | - |
| Algorithm | Paper | Optimization Type | Low-Level Optimizer | Code Resource |
|---|---|---|---|---|
| EvoGrad | Citterio, Beatrice FR, and Andrea Tangherloni. "EvoGrad: Metaheuristics in a Differentiable Wonderland." arXiv preprint arXiv:2506.06320 (2025). | SOP | - | EAs |
| Neural optimized metaheuristic algorithm | Oyelade, Olaide N., et al. "Deep learning at the service of metaheuristics for solving numerical optimization problems." Neural Computing and Applications (2025). | SOP | - | - |
| ada-smoDE | Zhang H, Shi J, Sun J, et al. "A Gradient-based Method for Differential Evolution Parameter Control by Smoothing" Proceedings of the Genetic and Evolutionary Computation Conference Companion. (2024). | SOP | DE | - |
| Algorithm | Paper | Optimization Type | Low-Level Optimizer | Code Resource |
|---|---|---|---|---|
| GNE | Ouyang, Kaichen, and Shengwei Fu. "Graph Neural Networks Are Evolutionary Algorithms." arXiv preprint arXiv:2412.17629 (2024). | SOP | EAs | - |
| Diffusion Evolution | Zhang, Yanbo, et al. "Diffusion Models are Evolutionary Algorithms" arXiv preprint arXiv:2410.02543 (2024). | SOP | EAs | Diffusion Evolution |
| RGD | Beckham, Christopher, et al. "Robust Guided Diffusion for Offline Black-Box Optimization" arXiv preprint arXiv:2410.00983 (2024). | SOP | - | RGD |
| GLHF | Li, Xiaobin, et al. "GLHF: General Learned Evolutionary Algorithm Via Hyper Functions." arXiv preprint arXiv:2405.03728 (2024). | SOP | DE | - |
| EvoTF | Lange, Robert Tjarko, Yingtao Tian, and Yujin Tang. "Evolution Transformer: In-Context Evolutionary Optimization." arXiv preprint arXiv:2403.02985 (2024). | SOP | - | RobertTLange/evosax |
| LEO | Yu, Peiyu, et al. "Latent Energy-Based Odyssey: Black-Box Optimization via Expanded Exploration in the Energy-Based Latent Space." arXiv preprint arXiv:2405.16730 (2024). | SOP | - | - |
| RIBBO | Song, Lei, et al. "Reinforced In-Context Black-Box Optimization." arXiv preprint arXiv:2402.17423 (2024). | SOP | - | RIBBO |
| NAP | Maraval, Alexandre, et al. "End-to-end meta-Bayesian optimisation with transformer neural processes." Advances in Neural Information Processing Systems 36 (2024). | SOP | - | - |
| DDOM | Krishnamoorthy, Siddarth, Satvik Mehul Mashkaria, and Aditya Grover. "Diffusion models for black-box optimization" International Conference on Machine Learning. PMLR, (2023). | SOP | - | DDOM |
| B2Opt | Li X, Wu K, Zhang X, et al. "B2Opt: Learning to Optimize Black-box Optimization with Little Budget". arXiv preprint arXiv:2304.11787, (2023). | SOP | GA | - |
| RNN-Opt | TV, Vishnu, et al. "Meta-learning for black-box optimization." Joint European Conference on Machine Learning and Knowledge Discovery in Databases. (2019). | SOP | - | - |
| RNN-OI | Chen, Yutian, et al. "Learning to learn without gradient descent by gradient descent." International Conference on Machine Learning. PMLR (2017). | SOP | - | - |
| Algorithm | Paper | Optimization Type | Low-Level Optimizer | Code Resource |
|---|---|---|---|---|
| MetaDE | Chen, Minyang, Chenchen Feng, and Ran Cheng. "MetaDE: Evolving Differential Evolution by Differential Evolution." IEEE Transactions on Evolutionary Computation (2025). | SOP | DE | MetaDE |
| LQD | Faldor, Maxence, Robert Tjarko Lange, and Antoine Cully. "Discovering Quality-Diversity Algorithms via Meta-Black-Box Optimization." arXiv preprint arXiv:2502.02190 (2025). | SOP | QD | - |
| LES | Lange, Robert, et al. "Discovering evolution strategies via meta-black-box optimization." The Eleventh International Conference on Learning Representations. (2023). | SOP | CMA-ES | LES |
| Algorithm | Paper | Optimization Type | Low-Level Optimizer | Code Resource |
|---|---|---|---|---|
| LGA | Lange, Robert, et al. "Discovering attention-based genetic algorithms via meta-black-box optimization." Proceedings of the Genetic and Evolutionary Computation Conference. (2023). | SOP | GA | LGA |
| LTO-POMDP | Gomes H S, Léger B, Gagné C. "Meta learning black-box population-based optimizers". arXiv preprint arXiv:2103.03526 (2021). | SOP | - | LTO-POMDP |
| Algorithm | Paper | Optimization Type | Low-Level Optimizer | Code Resource |
|---|---|---|---|---|
| AS-LLM | Wu, Xingyu, et al. "Large language model-enhanced algorithm selection: towards comprehensive algorithm representation." International Joint Conference on Artificial Intelligence (2024). | SOP | - | - |
| Algorithm | Paper | Optimization Type | Low-Level Optimizer | Code Resource |
|---|---|---|---|---|
| LLMOA | Zhong, Rui, et al. "LLMOA: A novel large language model assisted hyper-heuristic optimization algorithm." Advanced Engineering Informatics 64 (2025). | SOP | DE | LLMOA |
| LLaMEA with controlled mutation | Yin, Haoran, et al. "Controlling the mutation in large language models for the efficient evolution of algorithms." arXiv preprint arXiv:2412.03250 (2024). | SOP | ES | - |
| LLMOA | Zhong, Rui, et al. "LLMOA: A novel large language model assisted hyper-heuristic optimization algorithm." Advanced Engineering Informatics 64 (2025). | SOP | DE | LLMOA |
| GeminiDE | Zhong, Rui, et al. "GeminiDE: A Novel Parameter Adaptation Scheme in Differential Evolution." 2024 6th International Conference on Data-driven Optimization of Complex Systems (DOCS). IEEE, (2024). | SOP | DE | GeminiDE |
| Algorithm | Paper | Optimization Type | Low-Level Optimizer | Code Resource |
|---|---|---|---|---|
| REMoH | Forniés-Tabuenca, Diego, et al. "REMoH: A Reflective Evolution of Multi-objective Heuristics approach via Large Language Models." arXiv preprint arXiv:2506.07759 (2025). | MOP | - | - |
| LLMOPT | Jiang, Caigao, et al. "LLMOPT: Learning to Define and Solve General Optimization Problems from Scratch" arXiv preprint arXiv:2410.13213 (2024). | SOP | - | caigaojiang/LLMOPT |
| FunSearch | Romera-Paredes B, Barekatain M, Novikov A, et al. "Mathematical discoveries from program search with large language models". Nature, (2024). | CO | - | - |
| LLM-EPS | Zhang R, Liu F, Lin X, et al. "Understanding the Importance of Evolutionary Search in Automated Heuristic Design with Large Language Models"International Conference on Parallel Problem Solving from Nature. (2024). | - | - | - |
| LLaMoCo | Ma, Zeyuan, et al. "LLaMoCo: Instruction Tuning of Large Language Models for Optimization Code Generation." arXiv preprint arXiv:2403.01131 (2024). | SOP | - | LLaMoCo-722A |
| LLaMEA | van Stein, Niki, and Thomas Bäck. "LLaMEA: A Large Language Model Evolutionary Algorithm for Automatically Generating Metaheuristics." arXiv preprint arXiv:2405.20132 (2024). | SOP | - | - |
| Evoprompting | Chen, Angelica, David Dohan, and David So. "Evoprompting: Language models for code-level neural architecture search." Advances in Neural Information Processing Systems 36 (2024). | SOP | - | - |
| OptiMUS | AhmadiTeshnizi A, Gao W, Udell M. "OptiMUS: Scalable Optimization Modeling with (MI) LP Solvers and Large Language Models" Forty-first International Conference on Machine Learning (2024). | MILP | - | teshnizi/OptiMUS |
| EoH | Liu, Fei, et al. "Evolution of Heuristics: Towards Efficient Automatic Algorithm Design Using Large Language Model." 41st International Conference on Machine Learning (2024). | CO | - | nobodynobodypaper/EoH |
| LLMOPT | Huang Y, Wu S, Zhang W, et al. "Autonomous Multi-Objective Optimization Using Large Language Model". arXiv preprint arXiv:2406.08987, (2024). | MOOP | - | - |
| AEL | Liu, Fei, et al. "Algorithm evolution using large language model." arXiv preprint arXiv:2311.15249 (2023). | CO | - | AEL |
| Algorithm | Paper | Optimization Type | Low-Level Optimizer | Code Resource |
|---|---|---|---|---|
| UniSO | Tan, Rong-Xi, et al. "Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings." arXiv preprint arXiv:2506.07109 (2025). | SOP | - | UniSO |
| Model Swarms | Feng, Shangbin, et al. "Model Swarms: Collaborative Search to Adapt LLM Experts via Swarm Intelligence" arXiv preprint arXiv:2410.11163 (2024). | SOP | PSO | - |
| EvoPrompt | Guo, Qingyan, et al. "Connecting large language models with evolutionary algorithms yields powerful prompt optimizers." The Twelfth International Conference on Learning Representations (2024). | SOP | GA, DE | beeevita/EvoPrompt |
| CCMO-LLM | Wang, Zeyi, et al. "Large Language Model-Aided Evolutionary Search for Constrained Multiobjective Optimization." International Conference on Intelligent Computing (2024). | CMOP | - | - |
| LEO | Brahmachary, Shuvayan, et al. "Large Language Model-Based Evolutionary Optimizer: Reasoning with elitism." arXiv preprint arXiv:2403.02054 (2024). | SOP | - | - |
| EvoLLM | Lange, Robert Tjarko, Yingtao Tian, and Yujin Tang. "Large Language Models As Evolution Strategies." Proceedings of the Genetic and Evolutionary Computation Conference Companion (2024). | SOP | - | - |
| LMEA | Liu, Shengcai, et al. "Large language models as evolutionary optimizers." IEEE Congress on Evolutionary Computation (2024). | SOP | - | - |
| MOEA/D-LLM | Liu, Fei, et al. "Large language model for multi-objective evolutionary optimization." arXiv preprint arXiv:2310.12541 (2023). | MOOP | MOEA/D | MOEA/D-LLM |
| OPRO | Yang, Chengrun, et al. "Large language models as optimizers." arXiv preprint arXiv:2309.03409 (2023). | SOP | - | OPRO |
| ELM | Lehman J, Gordon J, Jain S, et al. "Evolution through large models" Handbook of Evolutionary Machine Learning. (2023). | CO | - | - |
| ToLLM | Guo P F, Chen Y H, Tsai Y D, et al. "Towards optimizing with large language models". arXiv preprint arXiv:2310.05204, (2023). | SOP | - | - |
| Indicator | Paper |
|---|---|
| ECDF | López-Ibáñez M, Vermetten D, Dreo J, et al. "Using the empirical attainment function for analyzing single-objective black-box optimization algorithms". arXiv preprint arXiv:2404.02031 (2024). |
| EAF | da Fonseca V G, Fonseca C M. "A link between the multivariate cumulative distribution function and the hitting function for random closed sets". Statistics & probability letters (2002). |
| Feature | Paper |
|---|---|
| LAS | Liu F, Zhang Q, Tong X, et al. "Fitness Landscape of Large Language Model-Assisted Automated Algorithm Search". arXiv preprint arXiv:2504.19636, (2025). |
| NeurELA | Ma Z, Chen J, Guo H, et al. "Neural Exploratory Landscape Analysis". arXiv preprint arXiv:2408.10672 (2024). |
| DoE2Vec | van Stein B, Long F X, Frenzel M, et al. "Doe2vec: Deep-learning based features for exploratory landscape analysis" Proceedings of the Companion Conference on Genetic and Evolutionary Computation. (2023). |
| TransOpt | Cenikj G, Petelin G, Eftimov T. "TransOptAS: Transformer-Based Algorithm Selection for Single-Objective Optimization" Proceedings of the Genetic and Evolutionary Computation Conference Companion. (2024). |
| Deep ELA | Seiler M V, Kerschke P, Trautmann H. "Deep-ELA: Deep Exploratory Landscape Analysis with Self-Supervised Pretrained Transformers for Single-and Multi-Objective Continuous Optimization Problems". arXiv preprint arXiv:2401.01192 (2024). |
| LvsC ELA | Seiler M, Škvorc U, Cenikj G, et al. "Learned Features vs. Classical ELA on Affine BBOB Functions" International Conference on Parallel Problem Solving from Nature. (2024). |
| Comparable Feature | Long F X, Vermetten D, van Stein B, et al. "BBOB instance analysis: Landscape properties and algorithm performance across problem instances" International Conference on the Applications of Evolutionary Computation. (2023). |
| ISA | Smith-Miles K, Muñoz M A. "Instance space analysis for algorithm testing: Methodology and software tools". ACM Computing Surveys (2023). |
| ELA | Mersmann O, Bischl B, Trautmann H, et al. "Exploratory landscape analysis" Proceedings of the 13th annual conference on Genetic and evolutionary computation. (2011). |
| Algorithm | Paper | Learning paradigm | Automated algorithm design task | Code | Application |
|---|---|---|---|---|---|
| HVRP-MTWSTT | Nguyen D V A, Gunawan A, Misir M, et al. "Deep reinforcement learning for solving the stochastic e-waste collection problem". European Journal of Operational Research, 2025. | Meta-RL | - | - | e-waste collection problems |
| RPSO | Zhang, Zihang, et al. "Reinforcement learning-based particle swarm optimization for wind farm layout problems." Energy 313 (2024). | Meta-RL | Algorithm Configuration | RPSO | Wind farm layout problems |
| The investigation of the ability of OpenAI-ES | Lorenc, Matyáš. "Utilizing Evolution Strategies to Train Transformers in Reinforcement Learning." arXiv preprint arXiv:2501.13883 (2025). | - | - | Code | Training of Networks |
| DQLGA | Q. Chen and W. Ding, "A Genetic Algorithm Based on Deep Q-learning in Optimization of Remote Sensing Data Discretization" IEEE Transactions on Evolutionary Computation (2024) | Meta-RL | Algorithm Configuration | - | Remote Sensing |
| DQN-Based NSGA-II | Pei, C. H. I., et al. "Dynamic effect web generation for heterogeneous UAV cluster using DQN-based NSGA-II: Methods and applications." Chinese Journal of Aeronautics (2024) | Meta-RL | Algorithm Configuration | - | Unmanned Aerial Vehicles |
📦️This is a collection of MetaBBO papers and their corresponding code resources (👈️Click here for the paper list).
Black Box Optimization (BBO) refers to a class of optimization problems where the objective function is defined as $f: X \rightarrow \mathbb{R}$. The term "Black-Box" means that, although we can evaluate $f(X)$ for any $X$ within the search domain, we have no access to additional information such as the mathematical expression, gradients, or any structural details. The only available data comes from the input $X$ and the corresponding output $f(X)$. BBO problems can be categorized into SOP(singe-objective), MOOP(multi-objective), COP(constrained), CMOP(constrained multi-objective), MMOP(multi-modal), MMOOP(multi-modal multi-objective), LSOP(large-scale), LS-MOOP(large-scale multi-objective), CO(combinatorial) optimization problem and MILP(mixed integer linear programming) based on their specific characteristics.
🔥Meta-Black-Box-Optimization (MetaBBO) is an emerging research topic, leveraging the generalization power of Meta Learning to enhance the performance of existing BBO optimizers, or create new ones. 🚀By utilizing Meta Learning, the reliance on expert-level knowledge is reduced, highlighting the trend toward automated algorithm design in BBO.
🚩We warmly invite you to read our survey on MetaBBO, "Toward Automated Algorithm Design: A Survey and Practical Guide to Meta-Black-Box-Optimization," for more detailed insights into MetaBBO! Besides, if you find this repository useful, please cite it in your publications or projects as follows.
@article{ma2024toward,
title={Toward Automated Algorithm Design: A Survey and Practical Guide to Meta-Black-Box-Optimization},
author={Ma, Zeyuan and Guo, Hongshu and Gong, Yue-Jiao and Zhang, Jun and Tan, Kay Chen},
journal={IEEE Transactions on Evolutionary Computation},
year={2025}
}
👨💻👩💻We are a research team mainly focus on Meta-Black-Box-Optimization (MetaBBO), which assists automated algorithm design for Evolutionary Computation.
Here is our homepage and github. 🥰🥰🥰Please feel free to contact us—any suggestions are welcome!
✨️MetaBBO is rapidly evolving, and this is by no means a comprehensive list of papers, which would be continuously matained and updated.
If you want to update the list or have any question：
😘Join us in perfecting the MetaBBO papers and their code resources together!
👍️👍️👍️Many outstanding teams have developed excellent GitHub repositories for the Evolutionary Computation community, and we are pleased to share them here.
| Repository | About |
|---|---|
| MetaBox | MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning |
| LLM4Opt | A Collection on Large Language Models for Optimization |
| pypop7 | A Pure-Python Library for POPulation-based Black-Box Optimization |
| EvoX | Distributed GPU-Accelerated Framework for Evolutionary Computation. Comprehensive Library of Evolutionary Algorithms & Benchmark Problems |
| evosax | Evolution Strategies in JAX |
We first classify them by learning paradigm, followed by different automated algorithm design tasks.
All papers are sorted by year of publication.
*The complete list of IEEE CEC series can be access at ntu.edu.sg.
*The complete list of BBOB series can be access at numbbo.
| Algorithm | Paper | Optimization Type | Low-Level Optimizer | RL | Code Resource |
|---|---|---|---|---|---|
| TSRL-CMM | Liu X, Wang T, Zeng Z, et al. "Three stage based reinforcement learning for combining multiple metaheuristic algorithms". Swarm and Evolutionary Computation, 2025, 95: 101935. | SOP | EAs | Tabular Q-learning | TSRL-CMM |
| HHRL-MAR | Zhu N, Zhao F, Cao J. "A Hyperheuristic and Reinforcement Learning Guided Meta-heuristic Algorithm Recommendation" 2024 27th International Conference on Computer Supported Cooperative Work in Design (2024) | SOP | SI | Tabular Q-learning | - |
| R2-RLMOEA | Tahernezhad-Javazm F, Rankin D, Bois N D, et al. "R2 Indicator and Deep Reinforcement Learning Enhanced Adaptive Multi-Objective Evolutionary Algorithm". arXiv preprint arXiv:2404.08161 (2024). | MOOP | EAs | DDQN | - |
| RL-DAS | Guo, Hongshu, et al. "Deep Reinforcement Learning for Dynamic Algorithm Selection: A Proof-of-Principle Study on Differential Evolution." IEEE Transactions on Systems, Man, and Cybernetics: Systems (2024). | SOP | DE | PPO | RL-DAS |
| Algorithm | Paper | Optimization Type | Low-Level Optimizer | RL | Code Resource |
|---|---|---|---|---|---|
| MSDE | Lu, Yiling, et al. "A multi-strategy self-adaptive differential evolution algorithm for assembly hybrid flowshop lot-streaming scheduling with component sharing." Swarm and Evolutionary Computation 92 (2025). | SOP | DE | Tabular Q-learning | - |
| rlDE | Yang, Xu, et al. "Reinforcement learning Based Automated Design of Differential Evolution Algorithm for Black-box Optimization." arXiv preprint arXiv:2501.12881 (2025). | SOP | DE | DDQN | - |
| DRL-SAEA | Shao, Shuai, Ye Tian, and Yajie Zhang. "Deep reinforcement learning assisted surrogate model management for expensive constrained multi-objective optimization." Swarm and Evolutionary Computation 92 (2025). | ECMOP | SAEA | DQN | - |
| ConfigX | Guo, Hongshu, et al. "ConfigX: Modular Configuration for Evolutionary Algorithms via Multitask Reinforcement Learning." arXiv preprint arXiv:2412.07507 (2024). | SOP | EAs | PPO | - |
| RL_DNSABC | Ye, Tingyu, et al. "Reinforcement learning-driven dual neighborhood structure artificial bee colony algorithm for continuous optimization problem." Applied Soft Computing (2024) | SOP | ABC | Tabular Q-learning | - |
| QLBOS2N | Cheng, Shi, et al. "A Q-Learning Based Brainstorming Optimization Algorithm for Solving Multimodal Optimization Problems." IEEE Transactions on Consumer Electronics (2024). | MMOPs | BSO | Tabular Q-learning | - |
| AuDE | Cao, Zijian, et al. "An autonomous differential evolution based on reinforcement learning for cooperative countermeasures of unmanned aerial vehicles." Applied Soft Computing (2024). | SOP | DE | Tabular Q-learning | - |
| DRLCEA | Luo, Wenguan, et al. "Deep reinforcement learning-guided coevolutionary algorithm for constrained multiobjective optimization." Information Sciences (2024). | CMOP | EAs | DQN | - |
| HF | Pei J, Liu J, Mei Y. "Learning from Offline and Online Experiences: A Hybrid Adaptive Operator Selection Framework" Proceedings of the Genetic and Evolutionary Computation Conference. (2024). | SOP,CO | DE | DDQN | - |
| UES-CMAES-RL | Bolufé-Röhler A, Xu B. "Deep Reinforcement Learning for Smart Restarts in Exploration-Only Exploitation-Only Hybrid Metaheuristics Metaheuristics International Conference" (2024). | SOP | UES-CMAES | DQN | - |
| MTDE-L2T | Wu S H, Huang Y, Wu X, et al. "Learning to Transfer for Evolutionary Multitasking". arXiv preprint arXiv:2406.14359, (2024). | MTOP | EC | PPO | - |
| MSoRL | Wang X, Wang F, He Q, et al. "A multi-swarm optimizer with a reinforcement learning mechanism for large-scale optimization". Swarm and Evolutionary Computation (2024). | LSOP | PSO | Tabular Q-learning | - |
| MRL-MOEA | Wang, Jing, et al. "A Novel Multi-State Reinforcement Learning-Based Multi-Objective Evolutionary Algorithm." Information Sciences (2024). | MOOP | MOEA | Tabular Q-learning | - |
| RLEMMO | Lian, Hongqiao, et al. "RLEMMO: Evolutionary Multimodal Optimization Assisted By Deep Reinforcement Learning." Proceedings of the Genetic and Evolutionary Computation Conference (2024). | MMOP | DE | PPO | - |
| SA-DQN-DE | Liao, Zuowen, Qishuo Pang, and Qiong Gu. "Differential evolution based on strategy adaptation and deep reinforcement learning for multimodal optimization problems." Swarm and Evolutionary Computation 87 (2024): 101568. | MMOP | DE | DQN | - |
| PG-DE & PG-MPEDE | Zhang, Haotian, et al. "Learning to select the recombination operator for derivative-free optimization." Science China Mathematics (2024). | SOP | DE | REINFORCE | - |
| RLNS | Hong, Jiale, Bo Shen, and Anqi Pan. "A reinforcement learning-based neighborhood search operator for multi-modal optimization and its applications." Expert Systems with Applications (2024). | MMOP | SSA,PSO,EO | Tabular Q-learning | - |
| RLMODE | Yu, Xiaobing, et al. "Reinforcement learning-based differential evolution algorithm for constrained multi-objective optimization problems." Engineering Applications of Artificial Intelligence (2024). | MOOP | DE | Tabular Q-learning | - |
| GLEET | Ma, Zeyuan, et al. "Auto-configuring Exploration-Exploitation Tradeoff in Evolutionary Computation via Deep Reinforcement Learning." Proceedings of the Genetic and Evolutionary Computation Conference (2024). | SOP | DE,PSO | PPO | GLEET |
| RLMFEA | Li, Shuijia, et al. "Evolutionary multitasking via reinforcement learning." IEEE Transactions on Emerging Topics in Computational Intelligence 8.1 (2023). | MFO | EAs | Tabular Q-learning | - |
| RLHDE | Peng L, Yuan Z, Dai G, et al. "Reinforcement learning-based hybrid differential evolution for global optimization of interplanetary trajectory design". Swarm and Evolutionary Computation, (2023). | SOP | HLSHADE | Tabular Q-learning | - |
| AMODE-DRL | Li T, Meng Y, Tang L. "Scheduling of continuous annealing with a multi-objective differential evolution algorithm based on deep reinforcement learning". IEEE Transactions on Automation Science and Engineering (2023). | MOOP | MODE | DDQN+DDPG | - |
| MARLABC | Zhao F, Wang Z, Wang L, et al. "A multi-agent reinforcement learning driven artificial bee colony algorithm with the central controller". Expert Systems with Applications (2023). | SOP | ABC | Tabular Q-learning | - |
| CEDE-DRL | Hu Z, Gong W, Pedrycz W, et al. "Deep reinforcement learning assisted co-evolutionary differential evolution for constrained optimization". Swarm and Evolutionary Computation (2023). | SOP | CO-DE | DQN | - |
| RLDMDE | Yang, Qingyong, et al. "Dynamic multi-strategy integrated differential evolution algorithm based on reinforcement learning for optimization problems." Complex & Intelligent Systems (2023). | SOP | DE | Tabular Q-learning | - |
| RLMMDE | Han Y, Peng H, Mei C, et al. "Multi-strategy multi-objective differential evolutionary algorithm with reinforcement learning". Knowledge-Based Systems (2023). | MOOP | MOEA | Tabular Q-learning | - |
| MPSORL | Meng, Xiaoding, Hecheng Li, and Anshan Chen. "Multi-strategy self-learning particle swarm optimization algorithm based on reinforcement learning." Mathematical Biosciences and Engineering (2023). | SOP | PSO | Tabular Q-learning | - |
| IRLMFO | Zhao F, Wang Q, Wang L. "An inverse reinforcement learning framework with the Q-learning mechanism for the metaheuristic algorithm". Knowledge-Based Systems (2023). | SOP | MFO | IRL+Tabual Q-learning | - |
| RLAM | Yin, Shiyuan, et al. "Reinforcement-learning-based parameter adaptation method for particle swarm optimization." Complex & Intelligent Systems (2023). | SOP | PSO | DDPG | - |
| LADE | Liu X, Sun J, Zhang Q, et al. "Learning to learn evolutionary algorithm: A learnable differential evolution". IEEE Transactions on Emerging Topics in Computational Intelligence (2023). | SOP | DE | REINFORCE | - |
| MOEADRL | Gao, Mengqi, et al. "An efficient evolutionary algorithm based on deep reinforcement learning for large-scale sparse multiobjective optimization." Applied Intelligence (2023). | LS-MOOP | SpareEAs | A2C | - |
| Q-LSHADE | Zhang H, Sun J, Bäck T, et al. "Controlling Sequential Hybrid Evolutionary Algorithm by Q-Learning". IEEE Computational Intelligence Magazine (2023). | SOP | LSHADE | Tabular Q-learning | - |
| NRLPSO | Li, Wei, et al. "Reinforcement learning-based particle swarm optimization with neighborhood differential mutation strategy." Swarm and Evolutionary Computation (2023). | SOP | PSO | Tabular Q-learning | - |
| RL-SHADE | Fister I, Fister D, Fister Jr I. "Reinforcement learning-based differential evolution for global optimization Differential Evolution: From Theory to Practice" (2022). | SOP | SHADE | Tabular Q-learning | - |
| RL-HPSDE | Tan, Zhiping, et al. "Differential evolution with hybrid parameters and mutation strategies based on reinforcement learning." Swarm and Evolutionary Computation (2022): 101194. | SOP | DE | Tabular Q-learning | - |
| MOEA/D-DQN | Tian, Ye, et al. "Deep reinforcement learning based adaptive operator selection for evolutionary multi-objective optimization." IEEE Transactions on Emerging Topics in Computational Intelligence (2022). | MOOP | MOEA | DDQN | - |
| RL-CORCO | Hu Z, Gong W. "Constrained evolutionary optimization based on reinforcement learning using the objective function and constraints". Knowledge-Based Systems (2022). | COP | DE | Tabular Q-learning | - |
| MADAC | Xue, Ke, et al. "Multi-agent dynamic algorithm configuration." Advances in Neural Information Processing Systems (2022). | MOOP | MOEA/D | VDN | - |
| RLLPSO | Wang F, Wang X, Sun S. "A reinforcement learning level-based particle swarm optimization algorithm for large-scale optimization." Information Sciences (2022). | LSOP | PSO | Tabular Q-learning | - |
| RL-PSO | Wu, Di, and G. Gary Wang. "Employing reinforcement learning to enhance particle swarm optimization methods." Engineering Optimization (2022). | SOP | PSO | REINFORCE | - |
| RLEA-SSC | Xia H, Li C, Zeng S, et al. "A reinforcement-learning-based evolutionary algorithm using solution space clustering for multimodal optimization problems 2021 IEEE Congress on Evolutionary Computation (CEC). (2021). | MMOP | DE | Tabular Q-learning | - |
| DE-DQN | Tan, Zhiping, and Kangshun Li. "Differential evolution with mixed mutation strategy based on deep reinforcement learning." Applied Soft Computing (2021). | SOP | DE | Tabular Q-learning | - |
| LDE | Sun, Jianyong, et al. "Learning Adaptive Differential Evolution Algorithm from Optimization Experiences by Policy Gradient." IEEE Transactions on Evolutionary Computation (2021). | SOP | DE | REINFORCE | yierh/LDE |
| RLEPSO | Yin, Shiyuan, et al. "RLEPSO: Reinforcement learning based Ensemble particle swarm optimizer." Proceedings of the 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence. (2021). | SOP | PSO | DDPG | - |
| RLDE | Hu Z, Gong W, Li S. "Reinforcement learning-based differential evolution for parameters extraction of photovoltaic models." Energy Reports (2021). | SOP | DE | Tabular Q-learning | - |
| LRMODE | Huang Y, Li W, Tian F, et al. "A fitness landscape ruggedness multiobjective differential evolution algorithm with a reinforcement learning strategy". Applied Soft Computing (2020). | MOOP | DE | Tabular Q-learning | - |
| MARLwCMA | Sallam, Karam M., et al. "Evolutionary framework with reinforcement learning-based mutation adaptation." IEEE Access (2020). | SOP | DE | Tabular Q-learning | - |
| QLPSO | Xu Y, Pi D. "A reinforcement learning-based communication topology in particle swarm optimization." Neural Computing and Applications (2020). | SOP | PSO | Tabular Q-learning | - |
| LTO | Shala G, Biedenkapp A, Awad N, et al. "Learning step-size adaptation in CMA-ES." Parallel Problem Solving from Nature–PPSN XVI: 16th International Conference (2020). | SOP | CMA-ES | GPS | - |
| DE-DDQN | Sharma, Mudita, et al. "Deep reinforcement learning based parameter control in differential evolution." Proceedings of the Genetic and Evolutionary Computation Conference (2019). | SOP | DE | Tabular Q-learning | mudita11/DE-DDQN |
| QL-M/S-OPSO | Liu Y, Lu H, Cheng S, et al. "An adaptive online parameter control algorithm for particle swarm optimization based on reinforcement learning" 2019 IEEE congress on evolutionary computation (2019). | SOP,MOOP | PSO | Tabular Q-learning | - |
| DE-RLFR | Li, Zhihui, et al. "Differential evolution based on reinforcement learning with fitness ranking for solving multimodal multiobjective problems" Swarm and Evolutionary Computation (2019). | MMOOP | DE | Tabular Q-learning | - |
| RL-MOEA/D | Ning W, Guo B, Guo X, et al. "Reinforcement learning aided parameter control in multi-objective evolutionary algorithm based on decomposition". Progress in Artificial Intelligence (2018). | MOOP | MOEA/D | SARSA | - |
| QFA | Sadhu A K, Konar A, Bhattacharjee T, et al. "Synergism of firefly algorithm and Q-learning for robot arm path planning". Swarm and Evolutionary Computation (2018). | SOP | FA | Tabular Q-learning | - |
| RLMPSO | Samma H, Lim C P, Saleh J M. "A new reinforcement learning-based memetic particle swarm optimizer". Applied Soft Computing (2016). | SOP | PSO | Tabular Q-learning | - |
| Algorithm | Paper | Optimization Type | Low-Level Optimizer | RL | Code Resource |
|---|---|---|---|---|---|
| ALDes | Zhao, Qi, et al. "Automated Metaheuristic Algorithm Design with Autoregressive Learning." arXiv preprint arXiv:2405.03419 (2024). | SOP | - | - | - |
| SYMBOL | Chen, Jiacheng, et al. "Symbol: Generating Flexible Black-Box Optimizers through Symbolic Equation Learning." The Twelfth International Conference on Learning Representations. (2024). | SOP | - | PPO | SYMBOL |
| GSF | Yi, Wenjie, et al. "Automated design of metaheuristics using reinforcement learning within a novel general search framework." IEEE Transactions on Evolutionary Computation (2022) | CO | - | PPO\DQN | - |
| Algorithm | Paper | Optimization Type | Low-Level Optimizer | RL | Code Resource |
|---|---|---|---|---|---|
| MELBA | Chaybouti, Sofian, et al. "Meta-learning of Black-box Solvers Using Deep Reinforcement Learning." NeurIPS 2022, MetaLearn Workshop. (2022). | SOP | - | PPO | - |
| Algorithm | Paper | Optimization Type | Low-Level Optimizer | Code Resource |
|---|---|---|---|---|
| TransOptAS | Cenikj G, Petelin G, Eftimov T. "TransOptAS: Transformer-Based Algorithm Selection for Single-Objective Optimization" Proceedings of the Genetic and Evolutionary Computation Conference Companion (2024). | SOP | EAs,SI | - |
| ASF-ALLFV | Li Y, Liang J, Yu K, et al. "Adaptive local landscape feature vector for problem classification and algorithm selection". Applied Soft Computing, (2022). | SOP | EAs,SI | - |
| AR-BB | Tian Y, Peng S, Zhang X, et al. "A recommender system for metaheuristic algorithms for continuous optimization based on deep recurrent neural networks". IEEE transactions on artificial intelligence (2020). | SOP | EAs,SI | - |
| Meta-VRP | Gutierrez-Rodríguez A E, Conant-Pablos S E, Ortiz-Bayliss J C, et al. "Selecting meta-heuristics for solving vehicle routing problems with time windows via meta-learning". Expert Systems with Applications (2019). | CO | MOEA | - |
| Meta-MOP | Tian Y, Peng S, Rodemann T, et al. "Automated selection of evolutionary multi-objective optimization algorithms" 2019 IEEE Symposium Series on Computational Intelligence. (2019). | MOOP | MOEA | - |
| Meta-TSP | Kanda J Y, de Carvalho A C, Hruschka E R, et al. "Using meta-learning to recommend meta-heuristics for the traveling salesman problem" 2011 10th international conference on machine learning and applications and workshops. (2011). | CO | GA | - |
| Meta-QAP | Smith-Miles K A. "Towards insightful algorithm selection for optimisation using meta-learning concepts" 2008 IEEE international joint conference on neural networks. (2008). | CO | MMAS | - |
| Algorithm | Paper | Optimization Type | Low-Level Optimizer | Code Resource |
|---|---|---|---|---|
| EvoGrad | Citterio, Beatrice FR, and Andrea Tangherloni. "EvoGrad: Metaheuristics in a Differentiable Wonderland." arXiv preprint arXiv:2506.06320 (2025). | SOP | - | EAs |
| Neural optimized metaheuristic algorithm | Oyelade, Olaide N., et al. "Deep learning at the service of metaheuristics for solving numerical optimization problems." Neural Computing and Applications (2025). | SOP | - | - |
| ada-smoDE | Zhang H, Shi J, Sun J, et al. "A Gradient-based Method for Differential Evolution Parameter Control by Smoothing" Proceedings of the Genetic and Evolutionary Computation Conference Companion. (2024). | SOP | DE | - |
| Algorithm | Paper | Optimization Type | Low-Level Optimizer | Code Resource |
|---|---|---|---|---|
| GNE | Ouyang, Kaichen, and Shengwei Fu. "Graph Neural Networks Are Evolutionary Algorithms." arXiv preprint arXiv:2412.17629 (2024). | SOP | EAs | - |
| Diffusion Evolution | Zhang, Yanbo, et al. "Diffusion Models are Evolutionary Algorithms" arXiv preprint arXiv:2410.02543 (2024). | SOP | EAs | Diffusion Evolution |
| RGD | Beckham, Christopher, et al. "Robust Guided Diffusion for Offline Black-Box Optimization" arXiv preprint arXiv:2410.00983 (2024). | SOP | - | RGD |
| GLHF | Li, Xiaobin, et al. "GLHF: General Learned Evolutionary Algorithm Via Hyper Functions." arXiv preprint arXiv:2405.03728 (2024). | SOP | DE | - |
| EvoTF | Lange, Robert Tjarko, Yingtao Tian, and Yujin Tang. "Evolution Transformer: In-Context Evolutionary Optimization." arXiv preprint arXiv:2403.02985 (2024). | SOP | - | RobertTLange/evosax |
| LEO | Yu, Peiyu, et al. "Latent Energy-Based Odyssey: Black-Box Optimization via Expanded Exploration in the Energy-Based Latent Space." arXiv preprint arXiv:2405.16730 (2024). | SOP | - | - |
| RIBBO | Song, Lei, et al. "Reinforced In-Context Black-Box Optimization." arXiv preprint arXiv:2402.17423 (2024). | SOP | - | RIBBO |
| NAP | Maraval, Alexandre, et al. "End-to-end meta-Bayesian optimisation with transformer neural processes." Advances in Neural Information Processing Systems 36 (2024). | SOP | - | - |
| DDOM | Krishnamoorthy, Siddarth, Satvik Mehul Mashkaria, and Aditya Grover. "Diffusion models for black-box optimization" International Conference on Machine Learning. PMLR, (2023). | SOP | - | DDOM |
| B2Opt | Li X, Wu K, Zhang X, et al. "B2Opt: Learning to Optimize Black-box Optimization with Little Budget". arXiv preprint arXiv:2304.11787, (2023). | SOP | GA | - |
| RNN-Opt | TV, Vishnu, et al. "Meta-learning for black-box optimization." Joint European Conference on Machine Learning and Knowledge Discovery in Databases. (2019). | SOP | - | - |
| RNN-OI | Chen, Yutian, et al. "Learning to learn without gradient descent by gradient descent." International Conference on Machine Learning. PMLR (2017). | SOP | - | - |
| Algorithm | Paper | Optimization Type | Low-Level Optimizer | Code Resource |
|---|---|---|---|---|
| MetaDE | Chen, Minyang, Chenchen Feng, and Ran Cheng. "MetaDE: Evolving Differential Evolution by Differential Evolution." IEEE Transactions on Evolutionary Computation (2025). | SOP | DE | MetaDE |
| LQD | Faldor, Maxence, Robert Tjarko Lange, and Antoine Cully. "Discovering Quality-Diversity Algorithms via Meta-Black-Box Optimization." arXiv preprint arXiv:2502.02190 (2025). | SOP | QD | - |
| LES | Lange, Robert, et al. "Discovering evolution strategies via meta-black-box optimization." The Eleventh International Conference on Learning Representations. (2023). | SOP | CMA-ES | LES |
| Algorithm | Paper | Optimization Type | Low-Level Optimizer | Code Resource |
|---|---|---|---|---|
| LGA | Lange, Robert, et al. "Discovering attention-based genetic algorithms via meta-black-box optimization." Proceedings of the Genetic and Evolutionary Computation Conference. (2023). | SOP | GA | LGA |
| LTO-POMDP | Gomes H S, Léger B, Gagné C. "Meta learning black-box population-based optimizers". arXiv preprint arXiv:2103.03526 (2021). | SOP | - | LTO-POMDP |
| Algorithm | Paper | Optimization Type | Low-Level Optimizer | Code Resource |
|---|---|---|---|---|
| AS-LLM | Wu, Xingyu, et al. "Large language model-enhanced algorithm selection: towards comprehensive algorithm representation." International Joint Conference on Artificial Intelligence (2024). | SOP | - | - |
| Algorithm | Paper | Optimization Type | Low-Level Optimizer | Code Resource |
|---|---|---|---|---|
| LLMOA | Zhong, Rui, et al. "LLMOA: A novel large language model assisted hyper-heuristic optimization algorithm." Advanced Engineering Informatics 64 (2025). | SOP | DE | LLMOA |
| LLaMEA with controlled mutation | Yin, Haoran, et al. "Controlling the mutation in large language models for the efficient evolution of algorithms." arXiv preprint arXiv:2412.03250 (2024). | SOP | ES | - |
| LLMOA | Zhong, Rui, et al. "LLMOA: A novel large language model assisted hyper-heuristic optimization algorithm." Advanced Engineering Informatics 64 (2025). | SOP | DE | LLMOA |
| GeminiDE | Zhong, Rui, et al. "GeminiDE: A Novel Parameter Adaptation Scheme in Differential Evolution." 2024 6th International Conference on Data-driven Optimization of Complex Systems (DOCS). IEEE, (2024). | SOP | DE | GeminiDE |
| Algorithm | Paper | Optimization Type | Low-Level Optimizer | Code Resource |
|---|---|---|---|---|
| REMoH | Forniés-Tabuenca, Diego, et al. "REMoH: A Reflective Evolution of Multi-objective Heuristics approach via Large Language Models." arXiv preprint arXiv:2506.07759 (2025). | MOP | - | - |
| LLMOPT | Jiang, Caigao, et al. "LLMOPT: Learning to Define and Solve General Optimization Problems from Scratch" arXiv preprint arXiv:2410.13213 (2024). | SOP | - | caigaojiang/LLMOPT |
| FunSearch | Romera-Paredes B, Barekatain M, Novikov A, et al. "Mathematical discoveries from program search with large language models". Nature, (2024). | CO | - | - |
| LLM-EPS | Zhang R, Liu F, Lin X, et al. "Understanding the Importance of Evolutionary Search in Automated Heuristic Design with Large Language Models"International Conference on Parallel Problem Solving from Nature. (2024). | - | - | - |
| LLaMoCo | Ma, Zeyuan, et al. "LLaMoCo: Instruction Tuning of Large Language Models for Optimization Code Generation." arXiv preprint arXiv:2403.01131 (2024). | SOP | - | LLaMoCo-722A |
| LLaMEA | van Stein, Niki, and Thomas Bäck. "LLaMEA: A Large Language Model Evolutionary Algorithm for Automatically Generating Metaheuristics." arXiv preprint arXiv:2405.20132 (2024). | SOP | - | - |
| Evoprompting | Chen, Angelica, David Dohan, and David So. "Evoprompting: Language models for code-level neural architecture search." Advances in Neural Information Processing Systems 36 (2024). | SOP | - | - |
| OptiMUS | AhmadiTeshnizi A, Gao W, Udell M. "OptiMUS: Scalable Optimization Modeling with (MI) LP Solvers and Large Language Models" Forty-first International Conference on Machine Learning (2024). | MILP | - | teshnizi/OptiMUS |
| EoH | Liu, Fei, et al. "Evolution of Heuristics: Towards Efficient Automatic Algorithm Design Using Large Language Model." 41st International Conference on Machine Learning (2024). | CO | - | nobodynobodypaper/EoH |
| LLMOPT | Huang Y, Wu S, Zhang W, et al. "Autonomous Multi-Objective Optimization Using Large Language Model". arXiv preprint arXiv:2406.08987, (2024). | MOOP | - | - |
| AEL | Liu, Fei, et al. "Algorithm evolution using large language model." arXiv preprint arXiv:2311.15249 (2023). | CO | - | AEL |
| Algorithm | Paper | Optimization Type | Low-Level Optimizer | Code Resource |
|---|---|---|---|---|
| UniSO | Tan, Rong-Xi, et al. "Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings." arXiv preprint arXiv:2506.07109 (2025). | SOP | - | UniSO |
| Model Swarms | Feng, Shangbin, et al. "Model Swarms: Collaborative Search to Adapt LLM Experts via Swarm Intelligence" arXiv preprint arXiv:2410.11163 (2024). | SOP | PSO | - |
| EvoPrompt | Guo, Qingyan, et al. "Connecting large language models with evolutionary algorithms yields powerful prompt optimizers." The Twelfth International Conference on Learning Representations (2024). | SOP | GA, DE | beeevita/EvoPrompt |
| CCMO-LLM | Wang, Zeyi, et al. "Large Language Model-Aided Evolutionary Search for Constrained Multiobjective Optimization." International Conference on Intelligent Computing (2024). | CMOP | - | - |
| LEO | Brahmachary, Shuvayan, et al. "Large Language Model-Based Evolutionary Optimizer: Reasoning with elitism." arXiv preprint arXiv:2403.02054 (2024). | SOP | - | - |
| EvoLLM | Lange, Robert Tjarko, Yingtao Tian, and Yujin Tang. "Large Language Models As Evolution Strategies." Proceedings of the Genetic and Evolutionary Computation Conference Companion (2024). | SOP | - | - |
| LMEA | Liu, Shengcai, et al. "Large language models as evolutionary optimizers." IEEE Congress on Evolutionary Computation (2024). | SOP | - | - |
| MOEA/D-LLM | Liu, Fei, et al. "Large language model for multi-objective evolutionary optimization." arXiv preprint arXiv:2310.12541 (2023). | MOOP | MOEA/D | MOEA/D-LLM |
| OPRO | Yang, Chengrun, et al. "Large language models as optimizers." arXiv preprint arXiv:2309.03409 (2023). | SOP | - | OPRO |
| ELM | Lehman J, Gordon J, Jain S, et al. "Evolution through large models" Handbook of Evolutionary Machine Learning. (2023). | CO | - | - |
| ToLLM | Guo P F, Chen Y H, Tsai Y D, et al. "Towards optimizing with large language models". arXiv preprint arXiv:2310.05204, (2023). | SOP | - | - |
| Indicator | Paper |
|---|---|
| ECDF | López-Ibáñez M, Vermetten D, Dreo J, et al. "Using the empirical attainment function for analyzing single-objective black-box optimization algorithms". arXiv preprint arXiv:2404.02031 (2024). |
| EAF | da Fonseca V G, Fonseca C M. "A link between the multivariate cumulative distribution function and the hitting function for random closed sets". Statistics & probability letters (2002). |
| Feature | Paper |
|---|---|
| LAS | Liu F, Zhang Q, Tong X, et al. "Fitness Landscape of Large Language Model-Assisted Automated Algorithm Search". arXiv preprint arXiv:2504.19636, (2025). |
| NeurELA | Ma Z, Chen J, Guo H, et al. "Neural Exploratory Landscape Analysis". arXiv preprint arXiv:2408.10672 (2024). |
| DoE2Vec | van Stein B, Long F X, Frenzel M, et al. "Doe2vec: Deep-learning based features for exploratory landscape analysis" Proceedings of the Companion Conference on Genetic and Evolutionary Computation. (2023). |
| TransOpt | Cenikj G, Petelin G, Eftimov T. "TransOptAS: Transformer-Based Algorithm Selection for Single-Objective Optimization" Proceedings of the Genetic and Evolutionary Computation Conference Companion. (2024). |
| Deep ELA | Seiler M V, Kerschke P, Trautmann H. "Deep-ELA: Deep Exploratory Landscape Analysis with Self-Supervised Pretrained Transformers for Single-and Multi-Objective Continuous Optimization Problems". arXiv preprint arXiv:2401.01192 (2024). |
| LvsC ELA | Seiler M, Škvorc U, Cenikj G, et al. "Learned Features vs. Classical ELA on Affine BBOB Functions" International Conference on Parallel Problem Solving from Nature. (2024). |
| Comparable Feature | Long F X, Vermetten D, van Stein B, et al. "BBOB instance analysis: Landscape properties and algorithm performance across problem instances" International Conference on the Applications of Evolutionary Computation. (2023). |
| ISA | Smith-Miles K, Muñoz M A. "Instance space analysis for algorithm testing: Methodology and software tools". ACM Computing Surveys (2023). |
| ELA | Mersmann O, Bischl B, Trautmann H, et al. "Exploratory landscape analysis" Proceedings of the 13th annual conference on Genetic and evolutionary computation. (2011). |
| Algorithm | Paper | Learning paradigm | Automated algorithm design task | Code | Application |
|---|---|---|---|---|---|
| HVRP-MTWSTT | Nguyen D V A, Gunawan A, Misir M, et al. "Deep reinforcement learning for solving the stochastic e-waste collection problem". European Journal of Operational Research, 2025. | Meta-RL | - | - | e-waste collection problems |
| RPSO | Zhang, Zihang, et al. "Reinforcement learning-based particle swarm optimization for wind farm layout problems." Energy 313 (2024). | Meta-RL | Algorithm Configuration | RPSO | Wind farm layout problems |
| The investigation of the ability of OpenAI-ES | Lorenc, Matyáš. "Utilizing Evolution Strategies to Train Transformers in Reinforcement Learning." arXiv preprint arXiv:2501.13883 (2025). | - | - | Code | Training of Networks |
| DQLGA | Q. Chen and W. Ding, "A Genetic Algorithm Based on Deep Q-learning in Optimization of Remote Sensing Data Discretization" IEEE Transactions on Evolutionary Computation (2024) | Meta-RL | Algorithm Configuration | - | Remote Sensing |
| DQN-Based NSGA-II | Pei, C. H. I., et al. "Dynamic effect web generation for heterogeneous UAV cluster using DQN-based NSGA-II: Methods and applications." Chinese Journal of Aeronautics (2024) | Meta-RL | Algorithm Configuration | - | Unmanned Aerial Vehicles |