Title: Partition to Evolve: Niching-enhanced Evolution with LLMs for Automated Algorithm Discovery
Venue: NeurIPS 2025
Accepted: yes
Keywords: Automated algorithm discovery

Evolutionary computation

Large language model
Abstract: Large language model-assisted Evolutionary Search (LES) has emerged as a promising approach for Automated Algorithm Discovery (AAD). While many evolutionary search strategies have been developed for classic optimization problems, LES operates in abstract language spaces, presenting unique challenges for applying these strategies effectively. To address this, we propose a general LES framework that incorporates feature-assisted niche construction within abstract search spaces, enabling the seamless integration of niche-based search strategies from evolutionary computation. Building on this framework, we introduce PartEvo, an LES method that combines niche collaborative search and advanced prompting strategies to improve algorithm discovery efficiency. Experiments on both synthetic and real-world optimization problems show that PartEvo outperforms human-designed baselines and surpasses prior LES methods, such as Eoh and Funsearch. In particular, on resource scheduling tasks, PartEvo generates meta-heuristics with low design costs, achieving up to 90.1\% performance improvement over widely-used baseline algorithms, highlighting its potential for real-world applications.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes \"PartEvo\", a novel framework integrating niche-based evolutionary computation (EC) techniques with Large Language Model-assisted Evolutionary Search (LES) for Automated Algorithm Discovery (AAD). Recognizing the unique challenges posed by abstract language search spaces in LES, the authors introduce a feature-assisted niche construction method to efficiently partition and explore these spaces. The contributions include a general LES framework, the PartEvo methodology, and comprehensive evaluations demonstrating its superiority over existing human-designed meta-heuristics and other LES methods (e.g., Funsearch and Eoh) on both synthetic and real-world optimization tasks.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\n1. Novel Integration: The paper uniquely integrates niche-based EC methods with LES, addressing effectively the complexity and abstractness of language-based search spaces.\n2. Superior Performance: Experimental results consistently demonstrate PartEvo's superior performance across multiple benchmark tasks, significantly outperforming existing human-designed and LES-based methods.\n3. Robustness: PartEvo demonstrates strong stability and robustness, achieving consistent high-quality outcomes across varied experimental setups and diverse optimization tasks.\n\n**Weaknesses**\n\n1. The authors could provide more specific examples to better illustrate the proposed method and its advantages clearly.\n2. The absence of publicly available resources or clear reproducibility guidelines may limit validation and adoption by the broader research community.\n\nQUESTIONS:\n1. I have read the prompts in the appendix and seen the demonstration benchmark functions, but I am still unclear about how these elements work together in practice. Could you please provide a concrete example showing exactly how the prompts are combined with the benchmark tasks in your pipeline?\n2. Is it possible to extend this framework towards training a dedicated model, instead of using only a frozen LLM with prompting? For instance, could future work explore jointly fine-tuning or adapting the LLM during the evolutionary search?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 1, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-27"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents PartEvo, a novel Language-based Evolutionary Search (LES) method that improves algorithm discovery efficiency. It introduces abstract search space partitioning to enable structured niche construction for algorithm discovery, integrating evolutionary computation techniques to enhance sampling resource allocation. PartEvo combines verbal gradients with local and global search strategies to discover high-performing algorithms, demonstrating significant efficiency gains through the fusion of evolutionary computation with LES. The method shows superior performance on algorithm automatic discovery tasks across both synthetic and real-world optimization problems, outperforming human-designed meta-heuristic baselines and proving the value of feature-assisted niche construction, advanced prompting, and niche-based sampling. PartEvo particularly excels in scenarios with limited sampling budgets by efficiently partitioning language search spaces and constructing niches that enable more effective allocation of LLMs queries.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengthen**\n\n- Propose an approach integrating niche-based EC techniques that largely improve algorithm automatic discovery efficiency.\n- Comprehensive experiment results demonstrate that it out-performs existing baselines.\n\n**Weakness**\n\n- It would be helpful if the author could include experimental results on classical optimization problems such as TSP, job scheduling, etc.\n- Evaluation with LLMs of various capabilities would be necessary to determine how sensitive the approach is to different LLMs.\n\nQUESTIONS:\n- ReEvo is also mentioned as an AAD approach. Why wasn't it included as a baseline in your experiment?\n- How does PartEvo perform regarding time and cost/token consumption?\n- Line 267, 280: \"EoH\" vs. \"Eoh\"—please be consistent.\n\nLIMITATIONS:\nYes, in the appendix", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes an LLM-assisted evolutionary search (LES) framework for Automated Algorithm Discovery (AAD) that incorporates niching-based collaborative search in the LES abstract language space. The proposed approach (PartEvo) combines verbal gradients with search strategies, leading to high performing algorithms for the AAD task. The method is evaluated on both synthetic and real data, showing its advantage over human-designed metaheuristic algorithms.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. An interesting extension of LES algorithm through incorporation of EC-niching method.\n2. In experimental evaluation on 4 benchmark problems the methods performs comparably or better than the considered baselines.\n\n\nWeaknesses:\n1. The proposed approach lacks deeper investigation of parameter settings. For instance, what is the relation between $k$ and $m$, and how should their values be selected for a given problem at hand? \n2. The experimental evaluation is not sufficient. More diverse benchmarks (especially related to real-life problems) should be considered for a thorough assessment of PartEvo. Furthermore, mean and standard deviation results should be presented within Table 1 in the main paper.\n3. Besides standard population-based metaheuristics, PartEvo is evaluated only against two specialised methods, which does not allow for making definitive conclusions regarding its strengths and weaknesses. Other methods, e.g. [1], should be considered in the experimental assessment of the method. \n4. The contribution of the paper is not precisely stated. To my understanding it is an extension of LES approach by means of adding a niching algorithm, however the idea of niching in EC is clearly not novel. I understand that the novelty lies in the application of niching to a specific search space, however, this space specificity and the consequent difficulty of designing the niching method is not clearly explained in the paper.\n\n[1] ReEvo: Large Language Models as Hyper-Heuristics with Reflective Evolution, [NeurIPS 2024]\n\nQUESTIONS:\n1. Why did you consider k-Means algorithm and not other clustering methods?\n2. How internal parameters of PartEvo were selected? How sensitive is the method to their choice?\n3. Could you compare your approach with the one proposed in [1] (see Weaknesses)?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper discuss a popular topic in recent optimization community: using large language models for automated optimization algorithm design. The authors propose a refined procedure termed as PartEvo to improve existing works such as EoH and Funsearch. Inspired by the success of niching strategy in traditional evolutionary algorithms, the authors first propose adding niching-based searching process into EoH framework. To this end, the authors first address the challenge of measuring similarity in abstract program space, where two metrics are proposed: code similarity and thought similarity. The former is based on the BLEU score of the generated algorithm sourcecodes and the latter is based on the hidden feature of the thought text (as those in EoH) through a pretrained BERT model. Given the similarity metric, the authors further add two additional searching strategies into the algorithm evolution process, which provide effective program search within similar algorithm individuals and balance the exploration and exploitation tradeoff in the same time. The experimental results, including the comparison test and ablation study, demonstrates this work shows better performance than EoH and Funsearch, and its proposed niching-related technique is effective.\n\nSTRENGTHS AND WEAKNESSES:\nPros:\n\n1. The writing of this paper is generally clear. \n2. The motivation of this paper is properly backed up.\n3. The methodology, including the similarity metrics and the corresponding niching-based searching enhancement, is well-organized and technically rational.\n4. Considering existing LLM-assisted optimization framework, this work presents certain novelty and performance improvement. The potential audience of this work is hence moderate.\n\nCons:\n\n1. Overall, I think the contribution of this paper is incremental. Applying evolution-like thoughts for algorithm design is novel, such as EoH. Employing high-level evolutionary technique such niching, however, is incremental.\n2. I still think the authors overpromise the significance of this work, since they claim that “Importantly, the proposed framework is highly flexible and extensible.” (line 134), and “ Additionally, any feature and clustering method can be utilized” (lines 136-137). For a framework, at least two or more scenarios should be validated. I suggest the authors add more experimental analysis to validate the general usage of the proposed framework.\n3. There might be an unclear methodology issue. Given the two similarity metrics the authors have proposed, how to use them in the proposed framework? I can not find any detail of how to combine these two metrics, this further lead to my confusion on the experimental results in Table 1, are the results of PartEvo obtained by aggregating the results of PartEvo with CSV and PartEvo with TE? If so, the robustness of PartEvo would be highly dependent on the metric selection, which is somewhat not so ideal.\n4. Although in experiments, four optimization scenarios are validated, the experimental setting is still too simple. For example, in P1 and P2, the training instances and testing instances are “small size with simple structure (sphere function)”, which makes me doubt the generalization ability of the learned algorithm. Adding more diversity in your testset to validate the true potential of the generated optimizer.\n5. The experiments still lack broader comparison with existing automated algorithm design methods such as those using Genetic Programming and Reinforcement Learning. The training efficiency, the resource requirement and the final performance should be fairly discussed.  \n6. Review on the related works is quite limited. For “Automated algorithm discovery” part, many valuable papers should be recognized and carefully reviewed to respect the scientific integrity and historical evolution path in this field, such as\nvan Stein, N., & Bäck, T. (2024). Llamea: A large language model evolutionary algorithm for automatically generating metaheuristics. IEEE Transactions on Evolutionary Computation.\nYu, H., & Liu, J. (2024). Deep Insights into Automated Optimization with Large Language Models and Evolutionary Algorithms. arXiv preprint arXiv:2410.20848.\nMa, Z., Guo, H., Gong, Y. J., Zhang, J., & Tan, K. C. (2025). Toward automated algorithm design: A survey and practical guide to meta-black-box-optimization. IEEE Transactions on Evolutionary Computation.\n\nQUESTIONS:\nSee Strengths And Weaknesses above.\n\nLIMITATIONS:\nI strongly suggest the authors discuss the limitations of this paper. Using LLMs for algorithm design is promising. However, the true potential, the efficiency and the generalization boundary are very aspects that deserve in-depth analysis.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}