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Aimin Zhou

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18 papers

2 author rows

AAAI Conference 2026 Conference Paper

CLIP2Pose: Frozen CLIP as Semantic Guide for Domain Adaptive Pose Estimation

Jiawen Li
Fei Jiang
Dandan Zhu
Jinxin Shi
Aimin Zhou

Unsupervised domain adaptive pose estimation is a fundamental yet challenging task due to the need to transfer from labeled synthetic data to unlabeled real data. Nevertheless, the underlying pose semantics, which are governed by spatial structure, remain largely consistent across domains. This observation motivates the use of vision-language models, which provide domain-invariant representations that align well with high-level semantic concepts. Motivated by this, we propose CLIP2Pose, a novel framework that leverages the semantic robustness of frozen CLIP encoders to facilitate cross-domain generalization. We first introduce a semantic-driven prompt mechanism that encodes structural priors, domain-specific appearance, and instance-level context into the image representation. This guides the model to focus on semantically meaningful and structurally relevant features. Next, we propose a semantic modulation module that adaptively refines visual features by conditioning them on prompt-derived embeddings, enhancing alignment between semantics and visual patterns. To further bridge the modality and domain gaps, we design a directional alignment loss that encourages consistent structural reasoning across both vision and language representations. Extensive experiments on domain adaptive human body and hand pose benchmarks show that CLIP2Pose achieves state-of-the-art performance.

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IJCAI Conference 2025 Conference Paper

A Fast-Adaptive Cognitive Diagnosis Framework for Computerized Adaptive Testing Systems

Yuanhao Liu
Yiya You
Shuo Liu
Hong Qian
Ying Qian
Aimin Zhou

Computerized Adaptive Testing (CAT) measures student ability by iteratively selecting informative questions, with core components being the Cognitive Diagnosis Model (CDM) and selection strategy. Current research focuses on optimizing the selection strategy, assuming relatively accurate CDM results. However, existing static CDMs struggle with rapid and accurate diagnosis in the early stage of CAT. To this end, this paper proposes a Fast Adaptive Cognitive Diagnosis (FACD) framework, which incorporates dynamic collaborative and personalized diagnosis modules. Specifically, the collaborative module in FACD uses a dynamic response graph to quickly build student cognitive profiles, while the personalized module leverages each student's response sequence for robust and individualized diagnosis. Extensive experiments on real-world datasets show that, compared with existing static CDMs, FACD not only achieves superior prediction performance across various selection strategies with an improvement between roughly 5%-10% in the early stage of CAT, but also maintains a commendable inference speed.

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ICML Conference 2025 Conference Paper

ERICT: Enhancing Robustness by Identifying Concept Tokens in Zero-Shot Vision Language Models

Xinpeng Dong
Min Zhang 0068
Didi Zhu
Ye Jun Jian
Keli Zhang
Aimin Zhou
Fei Wu 0001
Kun Kuang 0001

Pre-trained vision-language models (VLMs) have revolutionized the field of machine learning, demonstrating exceptional performance across a wide range of tasks. However, their robustness remains vulnerable to the spurious-correlation problem. Existing works often involve fine-tuning the model with labeled data or relying on large language models (LLMs) to generate more complex prompts. Although effective to some extent, these methods introduce new challenges, including additional computational costs and dependence on the quality of prompts without fully utilizing the vision modality. To address these limitations, we propose a novel method named ERICT to Enhance model Robustness by Identifying Concept Tokens. ERICT mitigates spurious correlation directly in the inference stage and comprises two key steps: (1) Identify concept tokens capturing invariant features through auxiliary prompts to generate a token-level mask. (2) Apply the mask to the attention weights of the CLS token in the vision encoder to help the model focus on the relevant image region. Extensive experiments show that ERICT significantly improves the overall performance including that of the worst group, and achieves new state-of-the-art results.

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AAAI Conference 2025 Conference Paper

Expensive Multi-Objective Bayesian Optimization Based on Diffusion Models

Bingdong Li
Zixiang Di
Yongfan Lu
Hong Qian
Feng Wang
Peng Yang
Ke Tang
Aimin Zhou

Multi-objective Bayesian optimization (MOBO) has shown promising performance on various expensive multi-objective optimization problems (EMOPs). However, effectively modeling complex distributions of the Pareto optimal solutions is difficult with limited function evaluations. Existing Pareto set learning algorithms may exhibit considerable instability in such expensive scenarios, leading to significant deviations between the obtained solution set and the Pareto set (PS). In this paper, we propose a novel Composite Diffusion Model based Pareto Set Learning algorithm (CDM-PSL) for expensive MOBO. CDM-PSL includes both unconditional and conditional diffusion model for generating high-quality samples efficiently. Besides, we introduce a weighting method based on information entropy to balance different objectives. This method is integrated with a guiding strategy to appropriately balancing different objectives during the optimization process. Experimental results on both synthetic and real-world problems demonstrates that CDM-PSL attains superior performance compared with state-of-the-art MOBO algorithms.

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ICLR Conference 2025 Conference Paper

LLMOPT: Learning to Define and Solve General Optimization Problems from Scratch

Caigao Jiang
Xiang Shu
Hong Qian
Xingyu Lu
Jun Zhou
Aimin Zhou
Yang Yu

Optimization problems are prevalent across various scenarios. Formulating and then solving optimization problems described by natural language often requires highly specialized human expertise, which could block the widespread application of optimization-based decision making. To automate problem formulation and solving, leveraging large language models (LLMs) has emerged as a potential way. However, this kind of approach suffers from the issue of optimization generalization. Namely, the accuracy of most current LLM-based methods and the generality of optimization problem types that they can model are still limited. In this paper, we propose a unified learning-based framework called LLMOPT to boost optimization generalization. Starting from the natural language descriptions of optimization problems and a pre-trained LLM, LLMOPT constructs the introduced five-element formulation as a universal model for learning to define diverse optimization problem types. Then, LLMOPT employs the multi-instruction tuning to enhance both problem formalization and solver code generation accuracy and generality. After that, to prevent hallucinations in LLMs, such as sacrificing solving accuracy to avoid execution errors, the model alignment and self-correction mechanism are adopted in LLMOPT. We evaluate the optimization generalization ability of LLMOPT and compared methods across six real-world datasets covering roughly 20 fields such as health, environment, energy and manufacturing, etc. Extensive experiment results show that LLMOPT is able to model various optimization problem types such as linear/nonlinear programming, mixed integer programming, and combinatorial optimization, and achieves a notable 11.08% average solving accuracy improvement compared with the state-of-the-art methods. The code is available at https://github.com/caigaojiang/LLMOPT.

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ICLR Conference 2025 Conference Paper

SOO-Bench: Benchmarks for Evaluating the Stability of Offline Black-Box Optimization

Hong Qian
Yiyi Zhu
Xiang Shu
Shuo Liu
Yaolin Wen
Xin An
Huakang Lu
Aimin Zhou

Black-box optimization aims to find the optima through building a model close to the black-box objective function based on function value evaluation. However, in many real-world tasks, such as the design of molecular formulas and mechanical structures, it is perilous, costly, or even infeasible to evaluate the objective function value of an actively sampled solution. In this situation, optimization can only be conducted via utilizing offline historical data, which yields offline black-box optimization. Different from the traditional goal that is to pursue the optimal solution, this paper emphasizes that the goal of offline optimization is to stably surpass the offline dataset during optimization procedure. Although benchmarks called Design-Bench already exist in this emerging field, it can hardly evaluate the stability of offline optimization and mainly provides real-world offline tasks and the corresponding offline datasets. To this end, this paper proposes benchmarks named SOO-Bench (i.e., Stable Offline Optimization Benchmarks) for offline black-box optimization algorithms, so as to systematically evaluate the stability of surpassing the offline dataset under different data distributions. Along with SOO-Bench, we also propose a stability indicator to measure the degree of stability. Specifically, SOO-Bench includes various real-world offline optimization tasks and offline datasets under different data distributions, involving the fields of satellites, materials science, structural mechanics, and automobile manufacturing. Empirically, baseline and state-of-the-art algorithms are tested and analyzed on SOO-Bench. Hopefully, SOO-Bench is expected to serve as a catalyst for the rapid developments of more novel and stable offline optimization methods. The code is available at \url{https://github.com/zhuyiyi-123/SOO-Bench}.

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ICML Conference 2025 Conference Paper

Strong and Weak Identifiability of Optimization-based Causal Discovery in Non-linear Additive Noise Models

Mingjia Li 0002
Hong Qian
Tian-Zuo Wang
Shujun Li
Min Zhang 0068
Aimin Zhou

Causal discovery aims to identify causal relationships from observational data. Recently, optimization-based causal discovery methods have attracted extensive attention in the literature due to their efficiency in handling high-dimensional problems. However, we observe that optimization-based methods often perform well on certain problems but struggle with others. This paper identifies a specific characteristic of causal structural equations that determines the difficulty of identification in causal discovery and, in turn, the performance of optimization-based methods. We conduct an in-depth study of the additive noise model (ANM) and propose to further divide identifiable problems into strongly and weakly identifiable types based on the difficulty of identification. We also provide a sufficient condition to distinguish the two categories. Inspired by these findings, this paper further proposes GENE, a generic method for addressing strongly and weakly identifiable problems in a unified way under the ANM assumption. GENE adopts an order-based search framework that incorporates conditional independence tests into order fitness evaluation, ensuring effectiveness on weakly identifiable problems. In addition, GENE restricts the dimensionality of the effect variables to ensure scale invariance, a property crucial for practical applications. Experiments demonstrate that GENE is uniquely effective in addressing weakly identifiable problems while also remaining competitive with state-of-the-art causal discovery algorithms for strongly identifiable problems.

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NeurIPS Conference 2024 Conference Paper

A Simple yet Scalable Granger Causal Structural Learning Approach for Topological Event Sequences

Mingjia Li
Shuo Liu
Hong Qian
Aimin Zhou

In modern telecommunication networks, faults manifest as alarms, generating thousands of events daily. Network operators need an efficient method to identify the root causes of these alarms to mitigate potential losses. This task is challenging due to the increasing scale of telecommunication networks and the interconnected nature of devices, where one fault can trigger a cascade of alarms across multiple devices within a topological network. Recent years have seen a growing focus on causal approaches to addressing this problem, emphasizing the importance of learning a Granger causal graph from topological event sequences. Such causal graphs delineate the relations among alarms and can significantly aid engineers in identifying and rectifying faults. However, existing methods either ignore the topological relationships among devices or suffer from relatively low scalability and efficiency, failing to deliver high-quality responses in a timely manner. To this end, this paper proposes $S^2GCSL$, a simple yet scalable Granger causal structural learning approach for topological event sequences. $S^2GCSL$ utilizes a linear kernel to model activation interactions among various event types within a topological network, and employs gradient descent to efficiently optimize the likelihood function. Notably, it can seamlessly incorporate expert knowledge as constraints within the optimization process, which enhances the interpretability of the outcomes. Extensive experimental results on both large-scale synthetic and real-world problems verify the scalability and efficacy of $S^2GCSL$.

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AAAI Conference 2024 Conference Paper

Are You Concerned about Limited Function Evaluations: Data-Augmented Pareto Set Learning for Expensive Multi-Objective Optimization

Yongfan Lu
Bingdong Li
Aimin Zhou

Optimizing multiple conflicting black-box objectives simultaneously is a prevalent occurrence in many real-world applications, such as neural architecture search, and machine learning. These problems are known as expensive multi-objective optimization problems (EMOPs) when the function evaluations are computationally or financially costly. Multi-objective Bayesian optimization (MOBO) offers an efficient approach to discovering a set of Pareto optimal solutions. However, the data deficiency issue caused by limited function evaluations has posed a great challenge to current optimization methods. Moreover, most current methods tend to prioritize the quality of candidate solutions, while ignoring the quantity of promising samples. In order to tackle these issues, our paper proposes a novel multi-objective Bayesian optimization algorithm with a data augmentation strategy that provides ample high-quality samples for Pareto set learning (PSL). Specifically, it utilizes Generative Adversarial Networks (GANs) to enrich data and a dominance prediction model to screen out high-quality samples, mitigating the predicament of limited function evaluations in EMOPs. Additionally, we adopt the regularity model to expensive multi-objective Bayesian optimization for PSL. Experimental results on both synthetic and real-world problems demonstrate that our algorithm outperforms several state-of-the-art and classical algorithms.

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AAAI Conference 2024 Conference Paper

From Coarse to Fine: A Distillation Method for Fine-Grained Emotion-Causal Span Pair Extraction in Conversation

Xinhao Chen
Chong Yang
Changzhi Sun
Man Lan
Aimin Zhou

We study the problem of extracting emotions and the causes behind these emotions in conversations. Existing methods either tackle them separately or jointly model them at the coarse-grained level of emotions (fewer emotion categories) and causes (utterance-level causes). In this work, we aim to jointly extract more fine-grained emotions and causes. We construct a fine-grained dataset FG-RECCON, includes 16 fine-grained emotion categories and span-level causes. To further improve the fine-grained extraction performance, we propose to utilize the casual discourse knowledge in a knowledge distillation way. Specifically, the teacher model learns to predict causal connective words between utterances, and then guides the student model in identifying both the fine-grained emotion labels and causal spans. Experimental results demonstrate that our distillation method achieves state-of-the-art performance on both RECCON and FG-RECCON dataset.

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ECAI Conference 2024 Conference Paper

High-Dimensional Causal Bayesian Optimization

Yupeng Wu
Weiye Wang
Yangwenhui Zhang
Mingjia Li 0002
Yuanhao Liu
Hong Qian
Aimin Zhou

Causal global optimization (CGO) aims to complete optimization tasks through causal inference. In the high-dimensional CGO problems, traditional causal Bayesian optimization (CBO) methods struggle with the curse of dimensionality attributed to the number of variables in the causal graph, and scale inconsistency among Gaussian Process (GP) models. These issues limit the application of CBO in domains requiring optimization over large causal graphs. To address these limitations, this paper proposes a high-dimensional causal Bayesian optimization (HCBO) algorithm. To address the curse of dimensionality, HCBO introduces a submodularity indicator for variable subsets through the concept of causal intrinsic dimensionality (CID). It then uses the submodular optimization algorithm to find approximations of CID within polynomial sample complexity. Theoretically, we disclose a sufficient condition for CID’s existence. To address the issue of scale inconsistency among GP models, HCBO introduces a scale-normalized scoring function, ensuring stable identification of the optimal GP model corresponding to CID for intervention. Extensive experiments are conducted on high-dimensional synthetic and real-world tasks, i. e. , coral ecology and health. The existence of CID is verified across the datasets of all tasks. HCBO achieves state-of-the-art performance in CGO problems and can handle causal graphs at a scale 10 times larger than that manageable by previous CBO methods.

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AAAI Conference 2024 Conference Paper

Symbolic Cognitive Diagnosis via Hybrid Optimization for Intelligent Education Systems

Junhao Shen
Hong Qian
Wei Zhang
Aimin Zhou

Cognitive diagnosis assessment is a fundamental and crucial task for student learning. It models the student-exercise interaction, and discovers the students' proficiency levels on each knowledge attribute. In real-world intelligent education systems, generalization and interpretability of cognitive diagnosis methods are of equal importance. However, most existing methods can hardly make the best of both worlds due to the complicated student-exercise interaction. To this end, this paper proposes a symbolic cognitive diagnosis~(SCD) framework to simultaneously enhance generalization and interpretability. The SCD framework incorporates the symbolic tree to explicably represent the complicated student-exercise interaction function, and utilizes gradient-based optimization methods to effectively learn the student and exercise parameters. Meanwhile, the accompanying challenge is that we need to tunnel the discrete symbolic representation and continuous parameter optimization. To address this challenge, we propose to hybridly optimize the representation and parameters in an alternating manner. To fulfill SCD, it alternately learns the symbolic tree by derivative-free genetic programming and learns the student and exercise parameters via gradient-based Adam. The extensive experimental results on various real-world datasets show the superiority of SCD on both generalization and interpretability. The ablation study verifies the efficacy of each ingredient in SCD, and the case study explicitly showcases how the interpretable ability of SCD works.

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AAAI Conference 2024 Conference Paper

Wasserstein Differential Privacy

Chengyi Yang
Jiayin Qi
Aimin Zhou

Differential privacy (DP) has achieved remarkable results in the field of privacy-preserving machine learning. However, existing DP frameworks do not satisfy all the conditions for becoming metrics, which prevents them from deriving better basic private properties and leads to exaggerated values on privacy budgets. We propose Wasserstein differential privacy (WDP), an alternative DP framework to measure the risk of privacy leakage, which satisfies the properties of symmetry and triangle inequality. We show and prove that WDP has 13 excellent properties, which can be theoretical supports for the better performance of WDP than other DP frameworks. In addition, we derive a general privacy accounting method called Wasserstein accountant, which enables WDP to be applied in stochastic gradient descent (SGD) scenarios containing subsampling. Experiments on basic mechanisms, compositions and deep learning show that the privacy budgets obtained by Wasserstein accountant are relatively stable and less influenced by order. Moreover, the overestimation on privacy budgets can be effectively alleviated. The code is available at https://github.com/Hifipsysta/WDP.

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ECAI Conference 2023 Conference Paper

Degradation-Resistant Offline Optimization via Accumulative Risk Control

Huakang Lu
Hong Qian
Yupeng Wu
Ziqi Liu
Ya-Lin Zhang 0001
Aimin Zhou
Yang Yu 0001

Offline optimization aims to elaborately construct a solution that optimizes a black-box function with only access to the offline dataset. A typical manner of constructing the solution is to train a surrogate model of the black-box function on the offline dataset and optimize the solution guided by the surrogate model. However, this manner often encounters a fundamental challenge that the surrogate model could erroneously estimate out-of-distribution (OOD) solutions. Therefore, the optimizer would be misled to produce inferior solutions for online applications, i. e. , degradation of performance. To this end, this paper formalizes the risk of degradation for OOD solutions and proposes an accumulative risk controlled offline optimization (ARCOO) method. Specifically, ARCOO learns a surrogate model in conjunction with an energy model. The energy model characterizes the risk of degradation by learning on high-risk solutions and low-risk ones contrastively. In the optimization procedure, the behavior of the optimizer in each step is controlled by a risk suppression factor calculated via the energy model, which leads to the controllable accumulative risk. Theoretically, we justify the efficacy of energy for accumulative risk control. Extensive experiments on offline optimization tasks show that ARCOO surpasses state-of-the-art methods in both degradation-resistance and optimality of the output solution.

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AAAI Conference 2023 Conference Paper

High-Dimensional Dueling Optimization with Preference Embedding

Yangwenhui Zhang
Hong Qian
Xiang Shu
Aimin Zhou

In many scenarios of black-box optimization, evaluating the objective function values of solutions is expensive, while comparing a pair of solutions is relatively cheap, which yields the dueling black-box optimization. The side effect of dueling optimization is that it doubles the dimension of solution space and exacerbates the dimensionality scalability issue of black-box optimization, e.g., Bayesian optimization. To address this issue, the existing dueling optimization methods fix one solution when dueling throughout the optimization process, but it may reduce their efficacy. Fortunately, it has been observed that, in recommendation systems, the dueling results are mainly determined by the latent human preferences. In this paper, we abstract this phenomenon as the preferential intrinsic dimension and inject it into the dueling Bayesian optimization, resulting in the preferential embedding dueling Bayesian optimization (PE-DBO). PE-DBO decouples optimization and pairwise comparison via the preferential embedding matrix. Optimization is performed in the preferential intrinsic subspace with much lower dimensionality, while pairwise comparison is completed in the original dueling solution space. Theoretically, we disclose that the preference function can be approximately preserved in the lower-dimensional preferential intrinsic subspace. Experiment results verify that, on molecule discovery and web page recommendation dueling optimization tasks, the preferential intrinsic dimension exists and PE-DBO is superior in scalability compared with that of the state-of-the-art (SOTA) methods.

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ECAI Conference 2023 Conference Paper

QCCDM: A Q-Augmented Causal Cognitive Diagnosis Model for Student Learning

Shuo Liu 0017
Hong Qian
Mingjia Li 0002
Aimin Zhou

Cognitive diagnosis is vital for intelligent education to determine students’ knowledge mastery levels from their response logs. The Q-matrix, representing the relationships between exercises and knowledge attributes, improves the interpretability of cognitive diagnosis models. However, completing the Q-matrix poses an expensive and challenging task due to the fine-grained division of knowledge attributes. Moreover, a manually sparse Q-matrix can also compromise the accuracy and interpretability of deducing students’ mastery levels, especially for infrequently observed or unseen knowledge attributes. To address this issue, this paper proposes a Q-augmented Causal Cognitive Diagnosis Model (QCCDM) for student learning. Specifically, QCCDM incorporates the structure causal model (SCM) to capture the causality between students’ mastery levels on different attributes, which enables to infer their proficiency on rarely observed knowledge attributes with better accuracy and interpretability. Notably, with SCM, one can guide students on how to realize their self-improvement through intervention. Furthermore, we propose to augment the Q-matrix in QCCDM, which uses the manual Q-matrix as a prior to deduce the relationships between exercises and explicit as well as latent knowledge attributes, resulting in a complete and comprehensive assessment of students’ abilities. We assess the efficacy of Q-augmentation across the widely-used Q-based cognitive diagnosis models and conduct the ablation study. The extensive experimental results on real-world datasets show that QCCDM outperforms the compared methods in terms of both accuracy and interpretability.

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ICML Conference 2022 Conference Paper

The Teaching Dimension of Regularized Kernel Learners

Hong Qian
Xu-Hui Liu
Chen-Xi Su
Aimin Zhou
Yang Yu 0001

Teaching dimension (TD) is a fundamental theoretical property for understanding machine teaching algorithms. It measures the sample complexity of teaching a target hypothesis to a learner. The TD of linear learners has been studied extensively, whereas the results of teaching non-linear learners are rare. A recent result investigates the TD of polynomial and Gaussian kernel learners. Unfortunately, the theoretical bounds therein show that the TD is high when teaching those non-linear learners. Inspired by the fact that regularization can reduce the learning complexity in machine learning, a natural question is whether the similar fact happens in machine teaching. To answer this essential question, this paper proposes a unified theoretical framework termed STARKE to analyze the TD of regularized kernel learners. On the basis of STARKE, we derive a generic result of any type of kernels. Furthermore, we disclose that the TD of regularized linear and regularized polynomial kernel learners can be strictly reduced. For regularized Gaussian kernel learners, we reveal that, although their TD is infinite, their epsilon-approximate TD can be exponentially reduced compared with that of the unregularized learners. The extensive experimental results of teaching the optimization-based learners verify the theoretical findings.

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AAAI Conference 2019 Conference Paper

Fuzzy-Classification Assisted Solution Preselection in Evolutionary Optimization

Aimin Zhou
Jinyuan Zhang
Jianyong Sun
Guixu Zhang

In evolutionary optimization, the preselection is an efficient operator to improve the search efficiency, which aims to filter unpromising candidate solutions before fitness evaluation. Most existing preselection operators rely on fitness values, surrogate models, or classification models. Basically, the classification based preselection regards the preselection as a classification procedure, i. e. , differentiating promising and unpromising candidate solutions. However, the difference between promising and unpromising classes becomes fuzzy as the running process goes on, as all the left solutions are likely to be promising ones. Facing this challenge, this paper proposes a fuzzy classification based preselection (FCPS) scheme, which utilizes the membership function to measure the quality of candidate solutions. The proposed FCPS scheme is applied to two state-of-the-art evolutionary algorithms on a test suite. The experimental results show the potential of FCPS on improving algorithm performance.

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