️This is a collection of [MetaBBO papers and their corresponding code resources](#content) (️Click here for the paper list). ## What is BBO?

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.

## What is MetaBBO? 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](http://arxiv.org/abs/2411.00625)," for more detailed insights into MetaBBO! Besides, if you find this repository useful, please cite it in your publications or projects as follows. \`\`\`latex @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\} \} \`\`\` ## Contact Us ‍‍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](https://gmc-drl.github.io/) and [github](https://github.com/GMC-DRL). **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： - Fork, Add, and Merge - ️Report an [issue](https://github.com/GMC-DRL/Awesome-MetaBBO/issues) - Contact WenJie Qiu ([wukongqwj@gmail.com](mailto:wukongqwj@gmail.com)) - **We warmly invite you to join our QQ group for further communication (Group Number: 952185139).** Join us in perfecting the MetaBBO papers and their code resources together! ## ️Useful Github Repository ️️️Many outstanding teams have developed excellent GitHub repositories for the Evolutionary Computation community, and we are pleased to share them here. |Repository|About| |:-:|:-:| |[MetaBox](https://github.com/GMC-DRL/MetaBox)|MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning| |[LLM4Opt](https://github.com/FeiLiu36/LLM4Opt)|A Collection on Large Language Models for Optimization| |[pypop7](https://github.com/Evolutionary-Intelligence/pypop)|A Pure-Python Library for POPulation-based Black-Box Optimization| |[EvoX](https://github.com/EMI-Group/evox)|Distributed GPU-Accelerated Framework for Evolutionary Computation. Comprehensive Library of Evolutionary Algorithms & Benchmark Problems| |[evosax](https://github.com/RobertTLange/evosax)|Evolution Strategies in JAX | # Content We first classify them by learning paradigm, followed by different automated algorithm design tasks. All papers are sorted by year of publication. - [1. Survey Papers & Benchmarks](#1-survey-papers--benchmarks) - [1.1. Survey Papers](#11-survey-papers) - [1.2. Benchmarks](#12-benchmarks) - [2. MetaBBO](#2-metabbo)

2.1 MetaBBO via Reinforcement Learning

- [2.1.1. Algorithm Selection](#211-algorithm-selection) - [2.1.2. Algorithm Configuration](#212-algorithm-configuration) - [2.1.3. Algorithm Generation](#213-algorithm-generation) - [2.1.4. Solution Manipulation](#214-solution-manipulation)

2.2 MetaBBO via Supervised Learning

- [2.2.1. Algorithm Selection](#221-algorithm-selection) - [2.2.2. Algorithm Configuration](#222-algorithm-configuration) - [2.2.3. Solution Manipulation](#223-solution-manipulation)

2.3 MetaBBO via Neuroevolution

- [2.3.1. Algorithm Configuration](#231-algorithm-configuration) - [2.3.2. Solution Manipulation](#232-solution-manipulation)

2.4 MetaBBO via In-Context Learning

- [2.4.1. Algorithm Selection](#241-algorithm-selection) - [2.4.2. Algorithm Configuration](#242-algorithm-configuration) - [2.4.3. Algorithm Generation](#243-algorithm-generation) - [2.4.4. Solution Manipulation](#244-solution-manipulation)

- [3. Others](#3-others) - [3.1 Evaluation Indicator](#31-evaluation-indicator) - [3.2 Landscape Feature](#32-landscape-feature) - ️[3.3 Application](#33-application) ## 1. Survey Papers \& Benchmarks ### 1.1. Survey Papers |Paper| |:-:| |Ma, Zeyuan, et al. "[**Toward Automated Algorithm Design: A Survey and Practical Guide to Meta-Black-Box-Optimization**](http://arxiv.org/abs/2411.00625)." arXiv preprint arXiv:2411.00625 (2024).| |Yang, Xu, Rui Wang, and Kaiwen Li. "[**Meta-Black-Box Optimization for Evolutionary Algorithms: Review and Perspective**](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4956956)" Available at SSRN 4956956 (2024).| |He Yu and Jing Liu. "[**Deep Insights into Automated Optimization with Large Language Models and Evolutionary Algorithms**](https://arxiv.org/abs/2410.20848)". arXiv preprint arXiv:2410.20848 (2024).| |Wu X, Wu S, Wu J, et al. "[**Evolutionary computation in the era of large language model: Survey and roadmap**](https://arxiv.org/abs/2401.10034)". arXiv preprint arXiv:2401.10034 (2024).| |Song, Xingyou, et al. "[**Position: Leverage Foundational Models for Black-Box Optimization**](https://openreview.net/forum?id=ea2MgKn3sV)" Forty-first International Conference on Machine Learning (2024)| |Huang, Sen, et al. "[**When Large Language Model Meets Optimization**](https://arxiv.org/abs/2405.10098)" arXiv preprint arXiv:2405.10098 (2024).| |Sun Q, Chen Z, Xu F, et al. "[**A Survey of Neural Code Intelligence: Paradigms, Advances and Beyond**](https://openreview.net/forum?id=lH99v8c72g)". CoRR, (2024).| |Huang S, Yang K, Qi S, et al. "[**When Large Language Model Meets Optimization**](https://arxiv.org/abs/2405.10098)". arXiv preprint arXiv:2405.10098 (2024).| |Li P, Hao J, Tang H, et al. "[**Bridging Evolutionary Algorithms and Reinforcement Learning: A Comprehensive Survey on Hybrid Algorithms**](https://ieeexplore.ieee.org/abstract/document/10637292). IEEE Transactions on Evolutionary Computation. (2024). |Song Y, Wu Y, Guo Y, et al. "[**Reinforcement learning-assisted evolutionary algorithm: A survey and research opportunities**](https://www.sciencedirect.com/science/article/pii/S2210650224000506). Swarm and Evolutionary Computation. (2024). |Nikolikj, Ana, et al. "[**Quantifying Individual and Joint Module Impact in Modular Optimization Frameworks**](https://arxiv.org/abs/2405.11964)." 2024 IEEE Congress on Evolutionary Computation. (2024). |Qian, Chao, Ke Xue, and Ren-Jian Wang. "[**Quality-Diversity Algorithms Can Provably Be Helpful for Optimization**](https://arxiv.org/abs/2401.10539)." arXiv preprint arXiv:2401.10539. (2024). |Huang, Beichen, et al. "[**Exploring the True Potential: Evaluating the Black-box Optimization Capability of Large Language Models**](https://arxiv.org/abs/2404.06290)." arXiv preprint arXiv:2404.06290. (2024). |Zhao Q, Duan Q, Yan B, et al. "[**Automated Design of Metaheuristic Algorithms: A Survey**](https://openreview.net/forum?id=qhtHsvF5zj)". Transactions on Machine Learning Research (2023).| |Chernigovskaya, Maria, Andrey Kharitonov, and Klaus Turowski. "[**A Recent Publications Survey on Reinforcement Learning for Selecting Parameters of Meta-Heuristic and Machine Learning Algorithms**](https://www.scitepress.org/Papers/2023/119543/119543.pdf)." CLOSER. (2023).| |Banzhaf W, Machado P. "[**Fundamentals of Evolutionary Machine Learning**](https://link.springer.com/chapter/10.1007/978-981-99-3814-8_1)" Handbook of Evolutionary Machine Learning. (2023).| |Drugan, Madalina M. "[**Reinforcement learning versus evolutionary computation: A survey on hybrid algorithms**](https://www.sciencedirect.com/science/article/abs/pii/S2210650217302766)." Swarm and Evolutionary Computation. (2019). |Stützle T, López-Ibáñez M. "[**Automated design of metaheuristic algorithms**](https://link.springer.com/chapter/10.1007/978-3-319-91086-4_17)". Handbook of metaheuristics (2019).| ### 1.2. Benchmarks |Benchmark|Paper|Code Resource|Optimization Type| |:-:|:-:|:-:|:-:| |SEvoBench|Yang Y, Zhao J, Yang T. "[**SEvoBench: A C++ Framework For Evolutionary Single-Objective Optimization Benchmarking**](https://arxiv.org/abs/2505.17430)." arXiv preprint arXiv:2505.17430, (2025).|[SEvoBench](https://github.com/yangyongkang2000/SEvoBench)|SOP| |Bencher|Papenmeier L, Nardi L. "[**Bencher: Simple and Reproducible Benchmarking for Black-Box Optimization**](https://arxiv.org/pdf/2505.21321)." arXiv preprint arXiv:2505.21321, (2025).|[Bencher](https://github.com/LeoIV/bencher)|SOP| |BLADE|van Stein N, Kononova A V, Yin H, et al. "[**BLADE: Benchmark suite for LLM-driven Automated Design and Evolution of iterative optimisation heuristics**](https://arxiv.org/pdf/2504.20183)." arXiv preprint arXiv:2504.20183, (2025).|-|SOP| |UAV-path-planning|Shehadeh, Mhd Ali, and Jakub Kudela. "[**Benchmarking global optimization techniques for unmanned aerial vehicle path planning**](https://arxiv.org/abs/2501.14503)." arXiv preprint arXiv:2501.14503 (2025).|-|SOP| |GP-based|He Y, Aranha C. "[**Evolving Benchmark Functions to Compare Evolutionary Algorithms via Genetic Programming**](https://arxiv.org/abs/2403.14146)". arXiv preprint arXiv:2403.14146 (2024).|[GP-based](https://github.com/Y1fanHE/cec2024)|SOP,MOOP| |SELECTOR|Benjamins C, Cenikj G, Nikolikj A, et al. "[**Instance selection for dynamic algorithm configuration with reinforcement learning: Improving generalization**](https://dl.acm.org/doi/abs/10.1145/3638530.3654291)" Proceedings of the Genetic and Evolutionary Computation Conference Companion. (2024).|[automl/instance-dac]( https://github.com/automl/instance-dac)|Comprehensive platform| |MetaBox|Ma, Zeyuan, et al. "[**MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning**](https://neurips.cc/virtual/2023/oral/73737)." Advances in Neural Information Processing Systems 36 (2023).|[GMC-DRL/MetaBox]( https://github.com/GMC-DRL/MetaBox)|Comprehensive platform| |NN-based|Prager R P, Dietrich K, Schneider L, et al. "[**Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features**](https://dl.acm.org/doi/abs/10.1145/3594805.3607136)" Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms (2023).| - |SOP,MOOP| |NeuroEvoBench|Lange, Robert, Yujin Tang, and Yingtao Tian. "[**Neuroevobench: Benchmarking evolutionary optimizers for deep learning applications**](https://neurips.cc/virtual/2023/oral/73737)." Advances in Neural Information Processing Systems 36 (2023)|[neuroevobench/neuroevobench](https://github.com/neuroevobench/neuroevobench)|Comprehensive platform| |MA-BBOB|Vermetten D, Ye F, Bäck T, et al. "[**MA-BBOB: A problem generator for black-box optimization using affine combinations and shifts**](https://dl.acm.org/doi/abs/10.1145/3673908)". ACM Transactions on Evolutionary Learning, (2024).|[Dvermetten/Many-affine-BBOB](https://github.com/Dvermetten/Many-affine-BBOB)|SOP,MOOP| |IEEE CEC 2022|Abhishek Kumar, Kenneth V. Price, Ali Wagdy Mohamed, Anas A. Hadi, P. N. Suganthan, "[**Problem definitions and evaluation criteria for the cec 2022 Special Session and Competition on Single Objective Bound Constrained Numerical Optimization**](https://www3.ntu.edu.sg/home/epnsugan/index_files/CEC2022/CEC2022.htm)." Technical Report 2022|[P-N-Suganthan/2022-SO-BO](https://github.com/P-N-Suganthan/2022-SO-BO)|SOP| |Affine Recombination|Dietrich K, Mersmann O. "[**Increasing the diversity of benchmark function sets through affine recombination**](https://link.springer.com/chapter/10.1007/978-3-031-14714-2_41)" International Conference on Parallel Problem Solving from Nature. (2022).| - |SOP,MOOP| |IEEE CEC 2021|Ali Wagdy, Anas A Hadi, Ali K. Mohamed, Prachi Agrawal, Abhishek Kumar and P. N. Suganthan, "[**Problem definitions and evaluation criteria for the cec 2021 Special Session and Competition on Single Objective Bound Constrained Numerical Optimization**](https://www3.ntu.edu.sg/home/epnsugan/index_files/CEC2021/CEC2021-2.htm)." Technical Report 2021|[P-N-Suganthan/2021-SO-BCO](https://github.com/P-N-Suganthan/2021-SO-BCO)|SOP| |Zigzag BBO|Kudela, Jakub. "[**Novel zigzag-based benchmark functions for bound constrained single objective optimization**](https://ieeexplore.ieee.org/abstract/document/9504720/)." 2021 IEEE Congress on Evolutionary Computation . IEEE, (2021).
Kudela, Jakub, and Radomil Matousek. "[**New benchmark functions for single-objective optimization based on a zigzag pattern**](https://ieeexplore.ieee.org/abstract/document/9684455/)." IEEE Access 10 (2022).|[JakubKudela89/Zigzag](https://github.com/JakubKudela89/Zigzag)|SOP| |HPOBench|Eggensperger, Katharina, et al. "[**HPOBench: A collection of reproducible multi-fidelity benchmark problems for HPO**](https://arxiv.org/abs/2109.06716)." arXiv preprint arXiv:2109.06716 (2021).|[automl/HPOBench](https://github.com/automl/HPOBench)|SOP,MOOP| |DACBench|Eimer, Theresa, et al. "[**DACBench: A benchmark library for dynamic algorithm configuration**](https://arxiv.org/abs/2105.08541)." arXiv preprint arXiv:2105.08541 (2021).|[automl/DACBench](https://github.com/automl/DACBench)|DAC| |Olympus|Häse, Florian, et al. "[**Olympus: a benchmarking framework for noisy optimization and experiment planning**](https://iopscience.iop.org/article/10.1088/2632-2153/abedc8/meta)." Machine Learning: Science and Technology (2021).|[aspuru-guzik-group/olympus](https://github.com/aspuru-guzik-group/olympus)|SOP,MOOP| |NeurIPS BBO challenge|Turner R, Eriksson D, McCourt M, et al. "[**Bayesian optimization is superior to random search for machine learning hyperparameter tuning: Analysis of the black-box optimization challenge 2020**](https://proceedings.mlr.press/v133/turner21a.html)" NeurIPS 2020 Competition and Demonstration Track. (2021)|[NeurIPS BBO challenge](https://github.com/rdturnermtl/bbo_challenge_starter_kit/) |SOP| |Random function generator|Tian Y, Peng S, Zhang X, et al. "[**A recommender system for metaheuristic algorithms for continuous optimization based on deep recurrent neural networks**](https://ieeexplore.ieee.org/abstract/document/9187549)". IEEE transactions on artificial intelligence (2020).|[Random function generator](https://github.com/BIMK/Algorithm-Recommendation) |SOP| |CEC 2020 competition on real-world optimization problem|Kumar A, Wu G, Ali M Z, et al. "[**A test-suite of non-convex constrained optimization problems from the real-world and some baseline results**](https://www.sciencedirect.com/science/article/pii/S2210650219308946). Swarm and Evolutionary Computation (2020).|[CEC 2020 real-world](https://github.com/P-N-Suganthan/2020-RW-Constrained-Optimisation)|-| |COCO|Hansen, Nikolaus, et al. "[**COCO: A platform for comparing continuous optimizers in a black-box setting**](https://www.tandfonline.com/doi/abs/10.1080/10556788.2020.1808977)." Optimization Methods and Software (2021).|[numbbo/coco](https://github.com/numbbo/coco)|SOP,MOOP| |EVOBBO|Muñoz, Mario A., and Kate Smith-Miles. "[**Generating new space-filling test instances for continuous black-box optimization**](https://direct.mit.edu/evco/article-abstract/28/3/379/94997)." Evolutionary computation (2020).|[andremun/EVOBBO_Instances](https://github.com/andremun/EVOBBO_Instances)|SOP、MOOP| |Bayesmark|Turner R, Eriksson D. "[**Bayesmark: Benchmark framework to easily compare bayesian optimization methods on real machine learning tasks**](https://bayesmark.readthedocs.io/en/latest/)." (2019). |[Bayesmark](https://github.com/uber/bayesmark)|SOP| |PBO|Doerr C, Ye F, Horesh N, et al. "[**Benchmarking discrete optimization heuristics with IOHprofiler**](https://dl.acm.org/doi/abs/10.1145/3319619.3326810)" Proceedings of the Genetic and Evolutionary Computation Conference Companion. (2019).|[PBO](https://github.com/IOHprofiler/IOHdata)|CO| |IOHprofiler (IOHexperimenter)|Doerr, Carola, et al. "[**IOHprofiler: A benchmarking and profiling tool for iterative optimization heuristics**](https://arxiv.org/abs/1810.05281)." arXiv preprint arXiv:1810.05281 (2018).
de Nobel, Jacob, et al. "[**Iohexperimenter: Benchmarking platform for iterative optimization heuristics**](https://direct.mit.edu/evco/article/doi/10.1162/evco_a_00342/116949)." Evolutionary Computation (2023): 1-6.|[IOHprofiler/
IOHexperimenter](https://github.com/IOHprofiler/IOHexperimenter)|Comprehensive platform| |MTMOOP|Yuan Y, Ong Y S, Feng L, et al. "[**Evolutionary multitasking for multiobjective continuous optimization: Benchmark problems, performance metrics and baseline results**](https://arxiv.org/abs/1706.02766)." arXiv preprint arXiv:1706.02766 (2017).|- |MTMO| |MTSOP|Da B, Ong Y S, Feng L, et al. "[**Evolutionary multitasking for single-objective continuous optimization: Benchmark problems, performance metric, and baseline results**](https://arxiv.org/abs/1706.03470)". arXiv preprint arXiv:1706.03470 (2017).|- |MTSO| |IEEE CEC 2017|N. H. Awad, M. Z. Ali, J. J. Liang, B. Y. Qu and P. N. Suganthan, "[**Problem definitions and evaluation criteria for the CEC 2017 competition on constrained real-parameter optimization**](https://www3.ntu.edu.sg/home/epnsugan/index_files/CEC2017/CEC2017.htm)." Technical Report (2017)|[P-N-Suganthan/CEC2017-BoundContrained](https://github.com/P-N-Suganthan/CEC2017-BoundContrained)|SOP,MOOP| |IEEE CEC 2015|J. J. Liang, B. Y. Qu, P. N. Suganthan, Q. Chen, "[**Problem Definitions and Evaluation Criteria for the CEC 2015 Competition on Learning-based Real-Parameter Single Objective Optimization**](https://www3.ntu.edu.sg/home/epnsugan/index_files/CEC2015/CEC2015.htm)", Technical Report, Computational Intelligence Laboratory (2015).|[P-N-Suganthan/CEC2015-Learning-Based](https://github.com/P-N-Suganthan/CEC2015-Learning-Based)|SOP,MOOP| |AClib|Hutter, Frank, et al. "[**AClib: A benchmark library for algorithm configuration**](https://link.springer.com/chapter/10.1007/978-3-319-09584-4_4)." Learning and Intelligent Optimization: 8th International Conference (2014).|[aclib.net](https://www.aclib.net/)|-| |IEEE CEC 2013|J. J. Liang, B-Y. Qu, P. N. Suganthan, Alfredo G. Hernández-Díaz, "[**Problem Definitions and Evaluation Criteria for the CEC 2013 Special Session and Competition on Real-Parameter Optimization**](https://www3.ntu.edu.sg/home/epnsugan/index_files/CEC2013/CEC2013.htm)", Technical Report, Computational Intelligence Laboratory (2013).|[P-N-Suganthan/CEC2013](https://github.com/P-N-Suganthan/CEC2013)|SOP,MOOP| |Protein–Docking|Hwang, Howook, et al. "[**Protein–protein docking benchmark version 4.0**](https://onlinelibrary.wiley.com/doi/abs/10.1002/prot.22830)." Proteins: Structure, Function, and Bioinformatics (2010).|[Protein–Docking](http://zlab.umassmed.edu/benchmark/)|-| |BBOB 2009|Hansen N, Finck S, Ros R, et al. "[**Real-parameter black-box optimization benchmarking 2009: Noiseless functions definitions**](https://inria.hal.science/inria-00362633/)". INRIA. (2009). |[BBOB 2009](https://web.archive.org/web/20200811021008/https://coco.gforge.inria.fr/doku.php?id=bbob-2009-results) |SOP,MOOP| |WFG|Huband S, Hingston P, Barone L, et al. "[**A review of multiobjective test problems and a scalable test problem toolkit**](https://ieeexplore.ieee.org/abstract/document/1705400)." IEEE Transactions on Evolutionary Computation. (2006).|[WFG](https://github.com/White-Chen/MOEA-Benchmark) |MOOP| |DTLZ|Deb K, Thiele L, Laumanns M, et al. "[**Scalable multi-objective optimization test problems**](https://ieeexplore.ieee.org/abstract/document/1007032)." Proceedings of the 2002 Congress on Evolutionary Computation (2002).|[DTLZ](https://github.com/msu-coinlab/pymop/tree/master?tab=readme-ov-file) |MOOP| |ZDT|Zitzler, E., Deb, K., and Thiele, L. "[**Comparison of Multiobjective Evolutionary Algorithms: Empirical Results**]( https://dl.acm.org/doi/10.1162/106365600568202)." Evolutionary Computation (2000). |[ZDT](https://github.com/White-Chen/MOEA-Benchmark)|MOOP| **The complete list of IEEE CEC series can be access at [ntu.edu.sg](https://www3.ntu.edu.sg/home/epnsugan/index_files/).* **The complete list of BBOB series can be access at [numbbo](https://numbbo.github.io/workshops/bbob2023.html).*

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## 2. MetaBBO ### 2.1 MetaBBO-RL #### 2.1.1 Algorithm Selection |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**](https://www.sciencedirect.com/science/article/abs/pii/S2210650225000938)". Swarm and Evolutionary Computation, 2025, 95: 101935.|SOP|EAs|Tabular Q-learning|[TSRL-CMM](https://github.com/xtongliu/TSRL-CMM-code)| |HHRL-MAR|Zhu N, Zhao F, Cao J. "[**A Hyperheuristic and Reinforcement Learning Guided Meta-heuristic Algorithm Recommendation**](https://ieeexplore.ieee.org/abstract/document/10580058/)" 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**](https://arxiv.org/abs/2404.08161)". 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**](https://ieeexplore.ieee.org/abstract/document/10496708/)." IEEE Transactions on Systems, Man, and Cybernetics: Systems (2024).|SOP|DE|PPO|[RL-DAS](https://github.com/GMC-DRL/RL-DAS)| #### 2.1.2 Algorithm Configuration |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**](https://www.sciencedirect.com/science/article/pii/S2210650224003213)." 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**](https://www.sciencedirect.com/science/article/pii/S2210650224003559)." Swarm and Evolutionary Computation 92 (2025).|ECMOP|SAEA|DQN|-| |ConfigX|Guo, Hongshu, et al. "[**ConfigX: Modular Configuration for Evolutionary Algorithms via Multitask Reinforcement Learning**](https://arxiv.org/abs/2412.07507)." 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**](https://www.sciencedirect.com/science/article/pii/S1568494624013759)." 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**](https://ieeexplore.ieee.org/abstract/document/10757322)." 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**](https://www.sciencedirect.com/science/article/pii/S1568494624013796)." Applied Soft Computing (2024).|SOP|DE|Tabular Q-learning|-| |DRLCEA|Luo, Wenguan, et al. "[**Deep reinforcement learning-guided coevolutionary algorithm for constrained multiobjective optimization**](https://www.sciencedirect.com/science/article/pii/S0020025524015627)." 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**](https://dl.acm.org/doi/abs/10.1145/3638529.3654062)" 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**](https://link.springer.com/chapter/10.1007/978-3-031-62922-8_2)" (2024).|SOP|UES-CMAES|DQN|-| |MTDE-L2T|Wu S H, Huang Y, Wu X, et al. "[**Learning to Transfer for Evolutionary Multitasking**](https://arxiv.org/abs/2406.14359)". 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**](https://www.sciencedirect.com/science/article/pii/S2210650224000191)". 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**](https://www.sciencedirect.com/science/article/pii/S0020025524013112)." Information Sciences (2024).|MOOP|MOEA|Tabular Q-learning|-| |RLEMMO|Lian, Hongqiao, et al. "[**RLEMMO: Evolutionary Multimodal Optimization Assisted By Deep Reinforcement Learning**](https://dl.acm.org/doi/abs/10.1145/3638529.3653995)." 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**](https://www.sciencedirect.com/science/article/pii/S2210650224001068)." 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**](https://link.springer.com/article/10.1007/s11425-023-2252-9)." 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**](https://www.sciencedirect.com/science/article/pii/S0957417424000150)." 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**](https://www.sciencedirect.com/science/article/pii/S0952197623020018)." 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**](https://dl.acm.org/doi/abs/10.1145/3638529.3653996)." Proceedings of the Genetic and Evolutionary Computation Conference (2024).|SOP|DE,PSO|PPO|[GLEET](https://github.com/GMC-DRL/GLEET)| |RLMFEA|Li, Shuijia, et al. "[**Evolutionary multitasking via reinforcement learning**](https://ieeexplore.ieee.org/abstract/document/10144924/)." 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**](https://www.sciencedirect.com/science/article/pii/S2210650223001244)". 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**](https://ieeexplore.ieee.org/abstract/document/10049395)". 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**](https://www.sciencedirect.com/science/article/pii/S0957417423001732)". 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**](https://www.sciencedirect.com/science/article/pii/S2210650223001608)". 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**](https://link.springer.com/article/10.1007/s40747-023-01243-9)." 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**](https://www.sciencedirect.com/science/article/pii/S0950705123005518)". 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**](http://www.aimspress.com/aimspress-data/mbe/2023/5/PDF/mbe-20-05-373.pdf)." 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**](https://www.sciencedirect.com/science/article/pii/S0950705123001181)". 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**](https://link.springer.com/article/10.1007/s40747-023-01012-8)." 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**](https://ieeexplore.ieee.org/abstract/document/10068274/)". 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**](https://link.springer.com/article/10.1007/s10489-023-04574-9)." 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**](https://ieeexplore.ieee.org/abstract/document/10035716/)". 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**](https://www.sciencedirect.com/science/article/pii/S2210650223000482)." 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**](https://link.springer.com/chapter/10.1007/978-981-16-8082-3_3)" (2022).|SOP|SHADE|Tabular Q-learning|-| |RL-HPSDE|Tan, Zhiping, et al. "[**Differential evolution with hybrid parameters and mutation strategies based on reinforcement learning**](https://www.sciencedirect.com/science/article/pii/S2210650222001602)." 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**](https://ieeexplore.ieee.org/abstract/document/9712324/)." 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**](https://www.sciencedirect.com/science/article/pii/S0950705121009709)". Knowledge-Based Systems (2022).|COP|DE|Tabular Q-learning|-| |MADAC|Xue, Ke, et al. "[**Multi-agent dynamic algorithm configuration**](https://proceedings.neurips.cc/paper_files/paper/2022/hash/7f02b39c0424cc4a422994289ca03e46-Abstract-Conference.html)." 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**](https://www.sciencedirect.com/science/article/pii/S0020025522004054)." 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**](https://www.tandfonline.com/doi/abs/10.1080/0305215X.2020.1867120)." 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**](https://ieeexplore.ieee.org/abstract/document/9504896) 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**](https://www.sciencedirect.com/science/article/abs/pii/S1568494621005998)." 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**](https://ieeexplore.ieee.org/abstract/document/9359652)." IEEE Transactions on Evolutionary Computation (2021).|SOP|DE|REINFORCE|[yierh/LDE](https://github.com/yierh/LDE)| |RLEPSO|Yin, Shiyuan, et al. "[**RLEPSO: Reinforcement learning based Ensemble particle swarm optimizer**](https://dl.acm.org/doi/abs/10.1145/3508546.3508599)." 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**](https://www.sciencedirect.com/science/article/pii/S2352484721000974)." 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**](https://www.sciencedirect.com/science/article/pii/S1568494620306311)". Applied Soft Computing (2020).|MOOP|DE|Tabular Q-learning|-| |MARLwCMA|Sallam, Karam M., et al. "[**Evolutionary framework with reinforcement learning-based mutation adaptation**](https://ieeexplore.ieee.org/abstract/document/9239320/)." IEEE Access (2020).|SOP|DE|Tabular Q-learning|-| |QLPSO|Xu Y, Pi D. "[**A reinforcement learning-based communication topology in particle swarm optimization**](https://link.springer.com/article/10.1007/s00521-019-04527-9)." 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**](https://link.springer.com/chapter/10.1007/978-3-030-58112-1_48)." 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**](https://dl.acm.org/doi/abs/10.1145/3321707.3321813)." Proceedings of the Genetic and Evolutionary Computation Conference (2019).|SOP|DE|Tabular Q-learning|[mudita11/DE-DDQN](https://github.com/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**](https://ieeexplore.ieee.org/abstract/document/8790035)" 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**](https://link.springer.com/article/10.1007/s13748-018-0155-7)". 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**](https://www.sciencedirect.com/science/article/pii/S2210650217306776)". 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**](https://www.sciencedirect.com/science/article/pii/S1568494616000132)". Applied Soft Computing (2016).|SOP|PSO|Tabular Q-learning|-| #### 2.1.3 Algorithm Generation |Algorithm|Paper|Optimization Type|Low-Level Optimizer|RL|Code Resource| |:-:|:-:|:-:|:-:|:-:|:-:| |ALDes|Zhao, Qi, et al. "[**Automated Metaheuristic Algorithm Design with Autoregressive Learning**](https://arxiv.org/abs/2405.03419)." arXiv preprint arXiv:2405.03419 (2024).|SOP|-|-|-| |SYMBOL|Chen, Jiacheng, et al. "[**Symbol: Generating Flexible Black-Box Optimizers through Symbolic Equation Learning**](https://openreview.net/forum?id=vLJcd43U7a)." The Twelfth International Conference on Learning Representations. (2024).|SOP|-|PPO|[SYMBOL](https://github.com/GMC-DRL/Symbol)| |GSF|Yi, Wenjie, et al. "[**Automated design of metaheuristics using reinforcement learning within a novel general search framework**](https://ieeexplore.ieee.org/abstract/document/9852781/)." IEEE Transactions on Evolutionary Computation (2022)|CO|-|PPO\DQN|-| #### 2.1.4 Solution Manipulation |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**](https://hal.science/hal-03930140/)." NeurIPS 2022, MetaLearn Workshop. (2022).|SOP|-|PPO|-|

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### 2.2 MetaBBO-SL #### 2.2.1 Algorithm Selection |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**](https://dl.acm.org/doi/abs/10.1145/3638530.3654191)" 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**](https://www.sciencedirect.com/science/article/pii/S1568494622008006)". 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**](https://ieeexplore.ieee.org/abstract/document/9187549)". 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**](https://www.sciencedirect.com/science/article/pii/S0957417418306857)". 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**](https://ieeexplore.ieee.org/abstract/document/9003018)" 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**](https://ieeexplore.ieee.org/abstract/document/6146996)" 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**](https://ieeexplore.ieee.org/abstract/document/4634391)" 2008 IEEE international joint conference on neural networks. (2008).|CO|MMAS|-| #### 2.2.2 Algorithm Configuration |Algorithm|Paper|Optimization Type|Low-Level Optimizer|Code Resource| |:-:|:-:|:-:|:-:|:-:| |EvoGrad|Citterio, Beatrice FR, and Andrea Tangherloni. "[**EvoGrad: Metaheuristics in a Differentiable Wonderland**](https://arxiv.org/pdf/2506.06320)." 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**](https://link.springer.com/article/10.1007/s00521-024-10610-7)." 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**](https://dl.acm.org/doi/abs/10.1145/3638530.3654185)" Proceedings of the Genetic and Evolutionary Computation Conference Companion. (2024).|SOP|DE|-| #### 2.2.3 Solution Manipulation |Algorithm|Paper|Optimization Type|Low-Level Optimizer|Code Resource| |:-:|:-:|:-:|:-:|:-:| |GNE|Ouyang, Kaichen, and Shengwei Fu. "[**Graph Neural Networks Are Evolutionary Algorithms**](https://arxiv.org/abs/2412.17629)." arXiv preprint arXiv:2412.17629 (2024).|SOP|EAs|-| |Diffusion Evolution|Zhang, Yanbo, et al. "[**Diffusion Models are Evolutionary Algorithms**](https://arxiv.org/abs/2410.02543)" arXiv preprint arXiv:2410.02543 (2024).|SOP|EAs|[Diffusion Evolution](https://github.com/Zhangyanbo/diffusion-evolution)| |RGD|Beckham, Christopher, et al. "[**Robust Guided Diffusion for Offline Black-Box Optimization**](https://arxiv.org/abs/2410.00983)" arXiv preprint arXiv:2410.00983 (2024).|SOP|-|[RGD](https://anonymous.4open.science/r/RGD-27A5/README.md)| |GLHF|Li, Xiaobin, et al. "[**GLHF: General Learned Evolutionary Algorithm Via Hyper Functions**](https://arxiv.org/abs/2405.03728)." arXiv preprint arXiv:2405.03728 (2024).|SOP|DE|-| |EvoTF|Lange, Robert Tjarko, Yingtao Tian, and Yujin Tang. "[**Evolution Transformer: In-Context Evolutionary Optimization**](https://arxiv.org/abs/2403.02985)." arXiv preprint arXiv:2403.02985 (2024).|SOP|-|[RobertTLange/evosax](https://github.com/RobertTLange/evosax)| |LEO|Yu, Peiyu, et al. "[**Latent Energy-Based Odyssey: Black-Box Optimization via Expanded Exploration in the Energy-Based Latent Space**](https://arxiv.org/abs/2405.16730)." arXiv preprint arXiv:2405.16730 (2024).|SOP|-|-| |RIBBO|Song, Lei, et al. "[**Reinforced In-Context Black-Box Optimization**](https://arxiv.org/abs/2402.17423)." arXiv preprint arXiv:2402.17423 (2024).|SOP|-|[RIBBO](https://github.com/songlei00/RIBBO)| |NAP|Maraval, Alexandre, et al. "[**End-to-end meta-Bayesian optimisation with transformer neural processes**](https://proceedings.neurips.cc/paper_files/paper/2023/hash/2561721d0ca69bab22b749cfc4f48f6c-Abstract-Conference.html)." Advances in Neural Information Processing Systems 36 (2024).|SOP|-|-| |DDOM|Krishnamoorthy, Siddarth, Satvik Mehul Mashkaria, and Aditya Grover. "[**Diffusion models for black-box optimization**](https://proceedings.mlr.press/v202/krishnamoorthy23a.html)" International Conference on Machine Learning. PMLR, (2023).|SOP|-|[DDOM](https://github.com/siddarthk97/ddom)| |B2Opt|Li X, Wu K, Zhang X, et al. "[**B2Opt: Learning to Optimize Black-box Optimization with Little Budget**](https://arxiv.org/abs/2304.11787)". arXiv preprint arXiv:2304.11787, (2023).|SOP|GA|-| |RNN-Opt|TV, Vishnu, et al. "[**Meta-learning for black-box optimization**](https://link.springer.com/chapter/10.1007/978-3-030-46147-8_22)." 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**](http://proceedings.mlr.press/v70/chen17e.html)." International Conference on Machine Learning. PMLR (2017).|SOP|-|-|

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### 2.3 MetaBBO-NE #### 2.3.1 Algorithm Configuration |Algorithm|Paper|Optimization Type|Low-Level Optimizer|Code Resource| |:-:|:-:|:-:|:-:|:-:| |MetaDE|Chen, Minyang, Chenchen Feng, and Ran Cheng. "[**MetaDE: Evolving Differential Evolution by Differential Evolution**](https://ieeexplore.ieee.org/abstract/document/10884874)." IEEE Transactions on Evolutionary Computation (2025).|SOP|DE|[MetaDE](https://github.com/EMI-Group/metade)| |LQD|Faldor, Maxence, Robert Tjarko Lange, and Antoine Cully. "[**Discovering Quality-Diversity Algorithms via Meta-Black-Box Optimization**](https://arxiv.org/abs/2502.02190)." arXiv preprint arXiv:2502.02190 (2025).|SOP|QD|-| |LES|Lange, Robert, et al. "[**Discovering evolution strategies via meta-black-box optimization**](https://iclr.cc/virtual/2023/poster/11005)." The Eleventh International Conference on Learning Representations. (2023).|SOP|CMA-ES|[LES](https://github.com/RobertTLange/evosax/blob/main/evosax/strategies/les.py)| #### 2.3.2 Solution Manipulation |Algorithm|Paper|Optimization Type|Low-Level Optimizer|Code Resource| |:-:|:-:|:-:|:-:|:-:| |LGA|Lange, Robert, et al. "[**Discovering attention-based genetic algorithms via meta-black-box optimization**](https://dl.acm.org/doi/abs/10.1145/3583131.3590496)." Proceedings of the Genetic and Evolutionary Computation Conference. (2023).|SOP|GA|[LGA](https://github.com/RobertTLange/evosax/blob/main/evosax/strategies/lga.py)| |LTO-POMDP|Gomes H S, Léger B, Gagné C. "[**Meta learning black-box population-based optimizers**](https://arxiv.org/abs/2103.03526)". arXiv preprint arXiv:2103.03526 (2021).|SOP|-|[LTO-POMDP](https://github.com/optimization-toolbox/meta-learning-population-based-optimizers)|

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### 2.4 MetaBBO-ICL #### 2.4.1 Algorithm Selection |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**](https://ira.lib.polyu.edu.hk/handle/10397/108348)." International Joint Conference on Artificial Intelligence (2024).|SOP|-|-| #### 2.4.2 Algorithm Configuration |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**](https://www.sciencedirect.com/science/article/pii/S1474034624006931)." Advanced Engineering Informatics 64 (2025).|SOP|DE|[LLMOA](https://github.com/RuiZhong961230/LLMOA)| |LLaMEA with controlled mutation|Yin, Haoran, et al. "[**Controlling the mutation in large language models for the efficient evolution of algorithms**](https://arxiv.org/abs/2412.03250)." arXiv preprint arXiv:2412.03250 (2024).|SOP|ES|-| |LLMOA|Zhong, Rui, et al. "[**LLMOA: A novel large language model assisted hyper-heuristic optimization algorithm**](https://www.sciencedirect.com/science/article/pii/S1474034624006931)." Advanced Engineering Informatics 64 (2025).|SOP|DE|[LLMOA](https://github.com/RuiZhong961230/LLMOA)| |GeminiDE|Zhong, Rui, et al. "[**GeminiDE: A Novel Parameter Adaptation Scheme in Differential Evolution**](https://ieeexplore.ieee.org/document/10704309)." 2024 6th International Conference on Data-driven Optimization of Complex Systems (DOCS). IEEE, (2024).|SOP|DE|[GeminiDE](https://github.com/RuiZhong961230/GeminiDE.)| #### 2.4.3 Algorithm Generation |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**](https://arxiv.org/pdf/2506.07759)." arXiv preprint arXiv:2506.07759 (2025).|MOP|-|-| |LLMOPT|Jiang, Caigao, et al. "[**LLMOPT: Learning to Define and Solve General Optimization Problems from Scratch**](https://arxiv.org/pdf/2410.13213)" arXiv preprint arXiv:2410.13213 (2024).|SOP|-|[caigaojiang/LLMOPT](https://github.com/caigaojiang/LLMOPT)| |FunSearch|Romera-Paredes B, Barekatain M, Novikov A, et al. "[**Mathematical discoveries from program search with large language models**](https://www.nature.com/articles/s41586-023-06924-6)". 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**](https://link.springer.com/chapter/10.1007/978-3-031-70068-2_12)"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**](https://arxiv.org/abs/2403.01131)." arXiv preprint arXiv:2403.01131 (2024).|SOP|-|[LLaMoCo-722A](https://anonymous.4open.science/r/LLaMoCo-722A)| |LLaMEA|van Stein, Niki, and Thomas Bäck. "[**LLaMEA: A Large Language Model Evolutionary Algorithm for Automatically Generating Metaheuristics**](https://arxiv.org/abs/2405.20132)." arXiv preprint arXiv:2405.20132 (2024).|SOP|-|-| |Evoprompting|Chen, Angelica, David Dohan, and David So. "[**Evoprompting: Language models for code-level neural architecture search**](https://proceedings.neurips.cc/paper_files/paper/2023/hash/184c1e18d00d7752805324da48ad25be-Abstract-Conference.html)." 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**](https://openreview.net/forum?id=YT1dtdLvSN)" Forty-first International Conference on Machine Learning (2024).|MILP|-|[teshnizi/OptiMUS](https://github.com/teshnizi/OptiMUS)| |EoH|Liu, Fei, et al. "[**Evolution of Heuristics: Towards Efficient Automatic Algorithm Design Using Large Language Model**](https://openreview.net/forum?id=BwAkaxqiLB)." 41st International Conference on Machine Learning (2024).|CO|-|[nobodynobodypaper/EoH](https://github.com/nobodynobodypaper/EoH)| |LLMOPT|Huang Y, Wu S, Zhang W, et al. "[**Autonomous Multi-Objective Optimization Using Large Language Model**](https://ennetix.cloud/?_=%2Fabs%2F2406.08987%23T54G%2B%2F%2FWDHwWKle2kogeMes%3D)". arXiv preprint arXiv:2406.08987, (2024).|MOOP|-|-| |AEL|Liu, Fei, et al. "[**Algorithm evolution using large language model**](https://arxiv.org/abs/2311.15249)." arXiv preprint arXiv:2311.15249 (2023).|CO|-|[AEL](https://paperswithcode.com/paper/algorithm-evolution-using-large-language)| #### 2.4.4 Solution Manipulation |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**](https://arxiv.org/pdf/2506.07109)." arXiv preprint arXiv:2506.07109 (2025).|SOP|-|[UniSO](https://github.com/lamda-bbo/universal-offline-bbo)| |Model Swarms|Feng, Shangbin, et al. "[**Model Swarms: Collaborative Search to Adapt LLM Experts via Swarm Intelligence**](https://arxiv.org/abs/2410.11163)" arXiv preprint arXiv:2410.11163 (2024).|SOP|PSO|-| |EvoPrompt|Guo, Qingyan, et al. "[**Connecting large language models with evolutionary algorithms yields powerful prompt optimizers**](https://openreview.net/forum?id=ZG3RaNIsO8)." The Twelfth International Conference on Learning Representations (2024).|SOP|GA, DE|[beeevita/EvoPrompt](https://github.com/beeevita/EvoPrompt)| |CCMO-LLM|Wang, Zeyi, et al. "[**Large Language Model-Aided Evolutionary Search for Constrained Multiobjective Optimization**](https://link.springer.com/chapter/10.1007/978-981-97-5581-3_18)." International Conference on Intelligent Computing (2024).|CMOP|-|-| |LEO|Brahmachary, Shuvayan, et al. "[**Large Language Model-Based Evolutionary Optimizer: Reasoning with elitism**](https://arxiv.org/abs/2403.02054)." arXiv preprint arXiv:2403.02054 (2024).|SOP|-|-| |EvoLLM|Lange, Robert Tjarko, Yingtao Tian, and Yujin Tang. "[**Large Language Models As Evolution Strategies**](https://dl.acm.org/doi/abs/10.1145/3638530.3654238)." Proceedings of the Genetic and Evolutionary Computation Conference Companion (2024).|SOP|-|-| |LMEA|Liu, Shengcai, et al. "[**Large language models as evolutionary optimizers**](https://arxiv.org/abs/2310.19046)." IEEE Congress on Evolutionary Computation (2024).|SOP|-|-| |MOEA/D-LLM|Liu, Fei, et al. "[**Large language model for multi-objective evolutionary optimization**](https://arxiv.org/abs/2310.12541)." arXiv preprint arXiv:2310.12541 (2023).|MOOP|MOEA/D|[MOEA/D-LLM](https://github.com/FeiLiu36/LLM4MOEA)| |OPRO|Yang, Chengrun, et al. "[**Large language models as optimizers**](https://arxiv.org/abs/2309.03409)." arXiv preprint arXiv:2309.03409 (2023).|SOP|-|[OPRO](https://github.com/google-deepmind/opro)| |ELM|Lehman J, Gordon J, Jain S, et al. "[**Evolution through large models**](https://link.springer.com/chapter/10.1007/978-981-99-3814-8_11)" 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**](https://arxiv.org/abs/2310.05204)". arXiv preprint arXiv:2310.05204, (2023).|SOP|-|-|

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## 3. Others ### 3.1 Evaluation Indicator |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**](https://arxiv.org/abs/2404.02031)". 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**](https://www.sciencedirect.com/science/article/pii/S0167715202000469)". Statistics & probability letters (2002).| ### 3.2 Landscape Feature |Feature|Paper| |:-:|:-:| |LAS|Liu F, Zhang Q, Tong X, et al. "[**Fitness Landscape of Large Language Model-Assisted Automated Algorithm Search**](https://arxiv.org/pdf/2504.19636)". arXiv preprint arXiv:2504.19636, (2025).| |NeurELA|Ma Z, Chen J, Guo H, et al. "[**Neural Exploratory Landscape Analysis**](https://arxiv.org/abs/2408.10672)". 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**](https://dl.acm.org/doi/abs/10.1145/3583133.3590609)" 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**](https://dl.acm.org/doi/abs/10.1145/3638530.3654191)" 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**](https://arxiv.org/abs/2401.01192)". 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**](https://link.springer.com/chapter/10.1007/978-3-031-70068-2_9)" 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**](https://link.springer.com/chapter/10.1007/978-3-031-30229-9_25)" 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**](https://dl.acm.org/doi/abs/10.1145/3572895)". ACM Computing Surveys (2023).| |ELA|Mersmann O, Bischl B, Trautmann H, et al. "[**Exploratory landscape analysis**](https://dl.acm.org/doi/abs/10.1145/2001576.2001690)" Proceedings of the 13th annual conference on Genetic and evolutionary computation. (2011).| ### 3.3 Application |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**](https://www.sciencedirect.com/science/article/abs/pii/S0377221725003182)". 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**](https://www.sciencedirect.com/science/article/pii/S0360544224038283)." Energy 313 (2024).|Meta-RL|Algorithm Configuration|[RPSO](https://toyamaailab.github.io/)|Wind farm layout problems| |The investigation of the ability of OpenAI-ES|Lorenc, Matyáš. "[**Utilizing Evolution Strategies to Train Transformers in Reinforcement Learning**](https://arxiv.org/abs/2501.13883)." arXiv preprint arXiv:2501.13883 (2025).|-|-|[Code](https://github.com/Mafi412/Evolution-Strategies-and-Decision-Transformers)|Training of Networks| |DQLGA|Q. Chen and W. Ding, "[**A Genetic Algorithm Based on Deep Q-learning in Optimization of Remote Sensing Data Discretization**](https://ieeexplore.ieee.org/document/10730790)" 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**](https://www.sciencedirect.com/science/article/pii/S1000936124005077)." Chinese Journal of Aeronautics (2024)|Meta-RL|Algorithm Configuration|-|Unmanned Aerial Vehicles|

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