Title: FSEO: Few-Shot Evolutionary Optimization via Meta-Learning for Expensive Multi-Objective Optimization
Venue: NeurIPS 2025
Accepted: yes
Keywords: Expensive optimization

meta-learning

few-shot optimization

multi-objective optimization
Abstract: Meta-learning has been demonstrated to be useful to improve the sampling efficiency of Bayesian optimization (BO) and surrogate-assisted evolutionary algorithms (SAEAs) when solving expensive optimization problems (EOPs). 
Existing studies mainly focus on either combinations of existing meta-learning modeling methods with optimization algorithms, or the development of meta-learning acquisition functions for specific meta BO. However, the meta-learning models used in the literature are not designed for optimization purpose, and the generalization ability of meta-learning acquisition functions is limited.
In this work, we develop a novel architecture of meta-learning model for optimization purpose and propose a generalized few-shot evolutionary optimization (FSEO) framework to solve EOPs.
We focus on the scenario of expensive multi-objective EOPs (EMOPs) in the context of few-shot optimization as there are few studies on it and its high requirement on surrogate modeling performance.
The surrogates in FSEO framework combines neural network with Gaussian Processes (GPs), their network parameters and some parameters of GPs represent task-independent experience and are meta-learned across related optimization tasks, the remaining GPs parameters are task-specific parameters that represent unique features of the target task.
We demonstrate that our FSEO framework is able to improve the sampling efficiency of existing SAEAs on EMOPs.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces FSEO, a framework for solving expensive multi-objective optimization problems (EMOPs) using few-shot meta-learning. At its core, the method builds a meta-learned surrogate model (MDKL) that combines a deep kernel architecture with Gaussian Processes. The surrogate is trained across a set of related tasks to learn task-independent parameters and then adapted online to each new target task with limited samples (as few as 1d evaluations). The surrogate is integrated into regression-based surrogate-assisted evolutionary algorithms (SAEAs), with MOEA/D-EGO used as a case study.\nThe authors validate their approach across synthetic DTLZ benchmarks and real-world tasks, including neural architecture search and engine calibration, and conduct ablation studies to explore the effects of task similarity and dataset size.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n\n* The need for few-shot sample-efficient optimization is strong, especially in high-cost engineering and machine learning settings.\n* FSEO is a modular enhancement that can be plugged into any regression-based SAEA, not tied to a specific optimizer.\n* MDKL introduces task-specific parameter adaptation during optimization, improving over frozen meta-learned surrogates used in past few-shot BO literature.\n* Comprehensive evaluation and the \"out-of-range\" setting shows that FSEO remains effective even when test tasks fall outside the meta-training distribution.\n\nWeaknesses\n\n* Performance inconsistency: Results on DTLZ4, DTLZ6, and especially DTLZ7 show that FSEO can struggle with non-uniform or discontinuous Pareto fronts — an important limitation.\n* No theoretical guarantees: The paper does not provide formal analysis (e.g., regret bounds, convergence guarantees) to support when/why MDKL works or fails.\n* Generalization to other SAEAs not demonstrated: All experiments focus on MOEA/D-EGO. Claims of framework generality would be stronger if results on a second SAEA were included.\n* Compute/resource use is under-reported: Adaptation cost per iteration and overall runtime are not measured, even though MDKL retraining happens repeatedly.\n\nQUESTIONS:\n1. Without an acquisition function, how does the evolutionary component balance uncertainty? Is the MDKL variance ever used directly in search?\n2. Do you have any quantitative measures of task similarity across DTLZ variants or real tasks (e.g., using function landscapes or mutual information)?\n3. How expensive is the adaptation step computationally? Could you report runtime per update or total time vs. baseline?\n4. In DTLZ7, you mention MDKL fails due to discontinuity. Have you considered hybrid approaches (e.g., combining meta-learned GP with density estimators)?\n5. Have you tried plugging MDKL into other SAEAs like RVEA or ParEGO? If not, are there any anticipated challenges?\n\n\nSuggestions for Improvement\n\n* Add a second SAEA to test generality of the FSEO framework.\n* Provide runtime measurements (total time, per-update cost, adaptation vs. inference).\n* Discuss whether task-specific adaptation ever overfits in low-data settings.\n* Add theoretical discussion or intuition about why MDKL generalizes across tasks.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces FSEO, a few-shot optimization framework based on meta-learning to solve expensive multi-objective optimization problems (EMOPs). The proposed framework uses a novel meta-learning surrogate model called Meta Deep Kernel Learning (MDKL), which combines neural networks with Gaussian Processes. The framework aims to improve the sample efficiency of surrogate-assisted evolutionary algorithms (SAEAs) by leveraging prior experience learned from small datasets of related tasks\nA meta learned surrogate that enables continual adaptation to new tasks\nA generalized framework for use in few-shot scenarios\nframework is shown to reduce the number of function evaluations needed while achieving comparable or better optimization performance compared to standard methods\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\nThe paper clearly targets a few-shot setting for expensive multi-objective optimization which is still an active area of research.  MDKL supports continual adaptation of task-specific parameters, which is architecturally significant.  Experiments were conducted on real world tasks where significant improvements have been shown. he paper includes controlled ablations for Task similarity (in-range vs. out-of-range).\n\nWeakness:\n\nThere is missing comparisons for other recent meta learning baselines.  There is no comparison to other meta-learning frameworks that target optimization (e.g., meta-acquisition functions or meta-BO on EMOPs), such as MALIBO, PREBO, or transfer-SAEA methods\nSynthetic results are only reported on DTLZ problems (DTLZ1–DTLZ7), which are standard but limited, No large-scale or high-dimensional EMOP benchmarks. \nThere are no much information on hyperparameters for reproduciability purpose, please direct me in rebuttal if I am missing them \nWhile IGD+ curves and error bands are provided in synthetic benchmarks (Figure 3), no error bars or statistical significance tests are shown.\n\nQUESTIONS:\nPlease refer to the strengths and weakness section to address questions.\n\nLIMITATIONS:\nPlease refer to the strengths and weakness section to address questions.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes a few-shot optimization approach for multi-objective optimization. It consists in learning a parameterized deep kernel learning model that learn surrogate for optimization. The approach is using the meta-learning procedure to estimate the parameters of the deep kernel learning to from the few samples available so far, in order to achieve good performance for multi-objective optimization. The approach is evaluated first on synthetic multi-objective optimization benchmark (DTLZ) and then on two more realistic problems (neural architecture search and engine calibration). Results show performance on par with the best approaches for each problem, while reducing the number of samples used for reaching good solutions for the optimization tasks.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- A sound proposal for few shot optimization, the overall method makes sense.\n- Good set of experiments to characterize the approach.\n- Results are generally comparable to state-of-the-art, except for some cases.\n\nWeaknesses:\n- Rather niche topic, the impact of the proposal is rather limited.\n- The experimental methodology is dubious, as the variants used to train the model are based on DTLZ benchmark, which is then used to assess the model. There are leakage risks, and performances are probably overestimated. I think the meta-training / meta-testing should be done over distincts DTLZ problems, the one used in training not be used again in testing. A leave-one-out methodology over can allow to make a better evaluation. Even with the extra experiments with task similarity used to avoid using close tasks for training-testing, I still think there are potential leakage issues that reduce confidence we can have over the results.\n- The proposal is not improving performances much, so it is not clear that the extra complexity added with the meta-learning worth it.\n- Related work section is not very helpful, not providing clear context to position the work.\n- Overall text quality can be improved, the writing is not always very clear. Careful proofreading would improve the quality of the paper.\n\nQUESTIONS:\n- Can you provide a clear context, providing a better overview of related work and how the current contribution differ from related work and constitue a meaningful contribution to the field? I found the discussion in Sec. 2 to be difficult to follow and not that helpful to proper position the work, there is a lot of very specific details without provide a clear high-level picture.\n- Can you provide a clear statement of the significance of the proposal and the reach of it? Said otherwise, “why should I care” about the current proposal.\n\nLIMITATIONS:\nThe proposal is quite algorithmic and has not clear and direct limitations and negative impact on society. Therefore, that point is not applicable to the paper.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nIn this paper, the authors propose a meta-learning-based approach for few-shot, expensive multi-objective optimization problems. The method leverages the meta-learning framework, which is well-suited for few-shot scenarios, and introduces additional components to handle the challenges of multi-objective optimization. A kernel function is used to learn from related tasks and is integrated with a neural network to capture more complex relationships. The learned knowledge is then transferred to task-specific parameters. Additionally, the authors propose a surrogate update strategy based on the mean squared error (MSE) of a given surrogate model. If the updated surrogate performs worse than the previous one, the model reverts to the earlier version. To demonstrate the effectiveness of their approach, the authors present extensive numerical results.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1: The authors effectively adopt the meta-learning framework to address multi-objective optimization problems.\n2: The surrogate update strategy is interesting, as it introduces a new way to evaluate update directions and discard less effective ones.\n3: Compared with the vanilla meta learning, the author more effectively uses prior experience.\n\nWeakness:\n1:The meta-learning approach is computationally intensive. The authors should provide a discussion of the associated computational cost and training time.\n2: The author claims this method is the model parameter adpation. However, the meta learning itself also a model adaption. I don't see the novelty claimed in this paper. It is more like an more effective meta learning method. It will be helpful if author can explain more on that.\n\nQUESTIONS:\nHow many related tasks are needed to train a good model, and how long does it take?\n\nCan you explain more on the model-parameter adapt and data adapt part?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}