IEEE Transactions on Evolutionary Computation最新文献_第3页

A Multimodal Ant Colony Optimization Framework for Multisolution Traveling Salesman Problem 多解旅行商问题的多模态蚁群优化框架

IF 15.9 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2026-10-01 Epub Date: 2025-11-28 DOI: 10.1109/TEVC.2025.3638360

Han-Yang Pan;Ming Li;Qiang Yang;Wei-Neng Chen;Ya-Hui Jia;Jian-Yu Li;Dan-Ting Duan;Dong Liu;Jun Zhang

{"title":"A Multimodal Ant Colony Optimization Framework for Multisolution Traveling Salesman Problem","authors":"Han-Yang Pan;Ming Li;Qiang Yang;Wei-Neng Chen;Ya-Hui Jia;Jian-Yu Li;Dan-Ting Duan;Dong Liu;Jun Zhang","doi":"10.1109/TEVC.2025.3638360","DOIUrl":"10.1109/TEVC.2025.3638360","url":null,"abstract":"This article devises a multimodal ACO (MACO) framework to solve multisolution traveling salesman problems (MSTSPs), which aim to find multiple optimal routes in a single run. Specifically, a dynamic niching strategy is designed to partition the ant colony into exclusive niches based on the route similarity measured by the common edges between two routes. Subsequently, MACO maintains a pheromone matrix for each niche and lets the ants in each niche construct their routes independently based on the associated pheromone matrix. For effective pheromone update, this article designs a niche and pheromone matrix association rule to match each newly partitioned niche with one pheromone matrix. To further improve optimization efficiency, this article designs a Dynamic Critical Edge Strategy to dig out critical edges during the optimization. Then, these critical edges are cooperated with 2-opt to refine the route constructed by each ant. To preserve the found optimal routes, this article devises an optimal route preservation strategy to store as many different high-quality routes as possible. Lastly, we instantiate MACO with four canonical ACO algorithms, namely Ant System(AS), Elite AS(EAS), Rank-based AS(ASRank), and Max-Min AS(MMAS), and then conduct extensive experiments to compare them with six state-of-the-art algorithms on the only one MSTSP benchmark suite and the TSPLIB instances. The experimental results reveal that the four MACOs perform competitively with one latest algorithm, but significantly outperform the other five comparison algorithms. Further analysis discovers that ASRank is the most effective one among the four ant colony optimizations (ACOs) to cooperate with MACO in solving MSTSPs.","PeriodicalId":13206,"journal":{"name":"IEEE Transactions on Evolutionary Computation","volume":"30 5","pages":"2168-2184"},"PeriodicalIF":15.9,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145613448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fuzzy Logic-Based Multifactorial Optimization for Evolutionary Multitasking 基于模糊逻辑的多因素进化多任务优化

IF 15.9 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2026-10-01 Epub Date: 2025-12-09 DOI: 10.1109/TEVC.2025.3641950

Wu Lin;Songbai Liu;Qiuzhen Lin;Liang Feng;Kay Chen Tan

{"title":"Fuzzy Logic-Based Multifactorial Optimization for Evolutionary Multitasking","authors":"Wu Lin;Songbai Liu;Qiuzhen Lin;Liang Feng;Kay Chen Tan","doi":"10.1109/TEVC.2025.3641950","DOIUrl":"10.1109/TEVC.2025.3641950","url":null,"abstract":"Multifactorial optimization (MFO) has gained increasing attention in evolutionary multitasking (EMT), as it enables implicit knowledge transfer among tasks to enhance overall optimization efficiency. However, selecting appropriate transfer methods and adjusting their transfer probabilities remain significant challenges in MFO. Since existing selection and adjustment rules are typically formulated based on classical logic, they often fail to effectively handle the inherent uncertainty and dynamic nature of EMT. To address these limitations, this article suggests a new multifactorial evolutionary algorithm based on fuzzy logic, called MFEA-FL. By employing the Mamdani fuzzy inference system (FIS), expert knowledge can be naturally incorporated into the decision-making process, enabling MFEA-FL to exhibit strong fault tolerance and robustness across a wide range of multitasking environments. Specifically, two strategies, namely fuzzy transfer method selection (FTMS) and fuzzy transfer probability adjustment (FTPA), are proposed. FTMS quantifies the applicability of candidate transfer methods via a Mamdani FIS and then adaptively selects the most promising one for conducting knowledge transfer. Meanwhile, FTPA estimates the change of the transfer probability via a Mamdani FIS and dynamically adjusts the intensity of knowledge transfer. Experimental results on a series of multitasking optimization problems validate the effectiveness of the two fuzzy strategies and the superior performance of MFEA-FL over existing state-of-the-art EMT algorithms.","PeriodicalId":13206,"journal":{"name":"IEEE Transactions on Evolutionary Computation","volume":"30 5","pages":"2108-2122"},"PeriodicalIF":15.9,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145717956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Collaborative Evolutionary Multitasking Multiobjective Adversarial Attacks in Hyperspectral Image Classification 高光谱图像分类中的协同进化多任务多目标对抗攻击

IF 15.9 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2026-10-01 Epub Date: 2025-08-18 DOI: 10.1109/TEVC.2025.3599560

Hao Li;Jingying Li;Maoguo Gong;Kelin Dang;A.K. Qin;Yue Wu;Lining Xing

{"title":"Collaborative Evolutionary Multitasking Multiobjective Adversarial Attacks in Hyperspectral Image Classification","authors":"Hao Li;Jingying Li;Maoguo Gong;Kelin Dang;A.K. Qin;Yue Wu;Lining Xing","doi":"10.1109/TEVC.2025.3599560","DOIUrl":"https://doi.org/10.1109/TEVC.2025.3599560","url":null,"abstract":"Adversarial attacks on hyperspectral image (HSI) classification has garnered significant attention. However, most of the existing adversarial attack methods tend to apply global perturbations to the HSI, ignoring the fact that different classes in the HSI have distinct preferences for the spectral bands, and fails to generate an approximate optimal perturbation for each class. Therefore, this article establishes an evolutionary multitasking multiobjective adversarial attack model to generate a universal adversarial perturbation for each class. Then, we propose a novel collaborative evolutionary multitasking optimization (CoEMTO) method to optimize all tasks collaboratively. In CoEMTO, the optimal solution is the combination of solutions for all tasks, and a dynamic reward resource allocation strategy is designed to approximate the optimal solution for all tasks. In addition, perturbations are applied to the abundance rather than directly manipulating the pixel spectra to ensure the physical consistency of the generated adversarial perturbations. Experimental results demonstrate that the proposed CoEMTO algorithm can efficiently converge toward the Pareto front, and significantly outperforms other adversarial attack methods in terms of attack effectiveness.","PeriodicalId":13206,"journal":{"name":"IEEE Transactions on Evolutionary Computation","volume":"30 5","pages":"1863-1877"},"PeriodicalIF":15.9,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Size-Imbalanced Dual Population With Complementary Search for Sparse Large-Scale Multimodal Multiobjective Optimization 基于互补搜索的规模不平衡双种群稀疏大规模多模态多目标优化

IF 15.9 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2026-10-01 Epub Date: 2025-10-07 DOI: 10.1109/TEVC.2025.3618953

Xiangyu Wang;Yaochu Jin

{"title":"A Size-Imbalanced Dual Population With Complementary Search for Sparse Large-Scale Multimodal Multiobjective Optimization","authors":"Xiangyu Wang;Yaochu Jin","doi":"10.1109/TEVC.2025.3618953","DOIUrl":"10.1109/TEVC.2025.3618953","url":null,"abstract":"Multimodal multiobjective optimization problem (multimodal MOPs (MMOPs)), which contain multiple distinct Pareto set, are found in many real-world applications. Their multimodality characteristics require algorithms to be specifically designed to enhance the diversity in the decision space. However, existing methods often struggle to achieve either desired diversity or satisfactory convergence speed, particularly in large-scale scenarios. To address this issue, we propose an evolutionary framework that employs a size-imbalanced dual population to balance the convergence and diversity in the decision space. Specifically, a large main population, guided by nonzero mask vectors, searches promising regions of the decision space for fast convergence. On the other hand, a small complementary population is guided by a mask vector that is bitwise complementary to those of the main population, enabling it to focus on the unexplored areas and ensure comprehensive coverage of the search space. Periodically, the two populations and their corresponding mask vectors are updated by inter- and intra-population information exchanges, allowing them to collaboratively adjust their search directions and effectively address multimodality challenges. Additionally, to further enhance the search efficiency, a clustering-enhanced genetic operator is designed to update different populations by adaptively applying either exploration or exploitation search phases. Last, we introduce a new evaluation metric, termed the multiobjective multimodal index, to evaluate the multimodality performance of algorithms in real-world MMOPs without reliance on ground-truth data. Experimental results show that the proposed method significantly outperforms five state-of-the-art algorithms on (non-) multimodal benchmarks and real-world applications with substantially reduced computational time. (The source code is available at <uri>https://github.com/XWang1216/MMEA-IDC</uri>.)","PeriodicalId":13206,"journal":{"name":"IEEE Transactions on Evolutionary Computation","volume":"30 5","pages":"1966-1980"},"PeriodicalIF":15.9,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145241481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learning to Decompose and Optimize for Large Scale Overlapping Problems 学习大规模重叠问题的分解与优化

IF 15.9 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2026-10-01 Epub Date: 2025-10-17 DOI: 10.1109/TEVC.2025.3622888

Maojiang Tian;Wei Du;Wenxuan Fang;Yang Tang;Yaochu Jin

{"title":"Learning to Decompose and Optimize for Large Scale Overlapping Problems","authors":"Maojiang Tian;Wei Du;Wenxuan Fang;Yang Tang;Yaochu Jin","doi":"10.1109/TEVC.2025.3622888","DOIUrl":"10.1109/TEVC.2025.3622888","url":null,"abstract":"large-scale overlapping problems (LSOPs) pose significant challenges in optimization due to the intricate interactions among the subcomponents. Traditional decomposition-based cooperative co-evolution (CC) algorithms generally rely on predefined rules or randomized grouping strategies, making it difficult to achieve reasonable and effective decomposition tailored to the specific characteristics of LSOPs. To address this, we propose a novel reinforcement learning (RL)-based framework, called RLDO, which utilizes a policy network to learn decomposition knowledge for efficient optimization. During each learning iteration, the policy network processes the features of the LSOP instance and generates the subcomponent interaction probability matrix. By sampling from this matrix, RLDO produces multiple decomposition results, which are subsequently integrated into the CC framework for optimization. The fitness values derived from the optimization process are normalized to serve as reward signals. Using the proximal policy optimization (PPO) algorithm, RLDO updates the policy network based on this feedback, enabling the network to learn more effective decomposition strategies. Finally, the well-trained policy network is capable of effectively decomposing and optimizing LSOPs with various complex topologies. Experimental results demonstrate that the proposed RLDO framework, with its interactive learning of decomposition mechanisms guided by optimization feedback, achieves significantly superior optimization performance compared to state-of-the-art LSOP algorithms.","PeriodicalId":13206,"journal":{"name":"IEEE Transactions on Evolutionary Computation","volume":"30 5","pages":"1937-1951"},"PeriodicalIF":15.9,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145310779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhanced Evolution of Parallel Algorithm Portfolio for Vehicle Routing Problem via Transfer Optimization 基于转移优化的车辆路径问题并行算法组合的改进演化

IF 15.9 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2026-10-01 Epub Date: 2025-10-01 DOI: 10.1109/TEVC.2025.3616385

Tong Guo;Yi Mei;Mengjie Zhang;Ke Tang;Kaiquan Cai;Wenbo Du

{"title":"Enhanced Evolution of Parallel Algorithm Portfolio for Vehicle Routing Problem via Transfer Optimization","authors":"Tong Guo;Yi Mei;Mengjie Zhang;Ke Tang;Kaiquan Cai;Wenbo Du","doi":"10.1109/TEVC.2025.3616385","DOIUrl":"10.1109/TEVC.2025.3616385","url":null,"abstract":"Parallel algorithm portfolio (PAP), comprising several component solvers with complementary capabilities, emerges as a cutting-edge computational technique for addressing computationally hard problems. Automatic construction of PAPs can develop high-performance PAPs without human intervention. It is natural to believe that the component solvers share common useful building blocks, thus knowledge transfer among them could be beneficial during the evolution. However, this has been neglected by existing studies. To fill this gap, we propose a transfer optimization framework for automatically co-evolving high-performance PAPs. Specifically, we develop a novel performance-oriented dynamic instance grouping strategy to divide problem instances into groups, each of which is associated with a subpopulation of individuals tasked with evolving a component solver. Additionally, the framework incorporates an adaptive knowledge transfer strategy that automatically identifies when and how to transfer knowledge among instance groups. We conducted extensive experiments on the well-known vehicle routing problem (VRP), a famously challenging NP-hard combinatorial optimization problem. The comprehensive experimental results from three public benchmarks demonstrate that our proposed framework significantly outperforms existing state-of-the-art VRP solvers and automatic PAP construction methods.","PeriodicalId":13206,"journal":{"name":"IEEE Transactions on Evolutionary Computation","volume":"30 5","pages":"1908-1922"},"PeriodicalIF":15.9,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145203270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IEEE Transactions on Evolutionary Computation Information for Authors IEEE演化计算资讯汇刊

IF 15.9 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2026-10-01 Epub Date: 2026-10-02 DOI: 10.1109/TEVC.2026.3730926

引用次数: 0

Dual Relational Surrogate-Assisted Evolution for Expensive Constrained Multiobjective Optimization 昂贵约束多目标优化的双关系代理辅助进化

IF 15.9 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2026-10-01 Epub Date: 2025-10-16 DOI: 10.1109/TEVC.2025.3622188

Songbai Liu;Peng Chen;Qiuzhen Lin

{"title":"Dual Relational Surrogate-Assisted Evolution for Expensive Constrained Multiobjective Optimization","authors":"Songbai Liu;Peng Chen;Qiuzhen Lin","doi":"10.1109/TEVC.2025.3622188","DOIUrl":"10.1109/TEVC.2025.3622188","url":null,"abstract":"Expensive constrained multiobjective optimization problems (ECMOPs) present significant challenges due to the high cost of evaluating both objective and constraint functions. Existing constrained multiobjective evolutionary algorithms (CMOEAs) often struggle with convergence and feasibility under strict evaluation budgets, while surrogate-assisted CMOEAs typically rely on regression models that do not align well with the ranking-based nature of evolutionary selection. To address these limitations, this article proposes RECMO, an adaptive multistage evolutionary framework driven by dual relational surrogate models. One model captures dominance relationships to promote convergence, while the other focuses on feasibility comparisons to guide constraint satisfaction. These surrogates are dynamically activated according to the stage of evolution: the convergence-oriented surrogate dominates in early search, the feasibility surrogate is gradually introduced in the middle stage when conflicts arise, and both are jointly leveraged in the late stage to ensure feasible and high-quality solutions. This selective activation enables RECMO to progressively shift the optimization emphasis from objective-driven exploration to feasibility enforcement in a resource-efficient manner. Extensive experiments on benchmark suites and 26 real-world ECMOPs demonstrate that RECMO consistently outperforms state-of-the-art algorithms in terms of convergence, diversity, and feasibility. Additional ablation studies and sensitivity analyzes further confirm the robustness and adaptability of the proposed framework.","PeriodicalId":13206,"journal":{"name":"IEEE Transactions on Evolutionary Computation","volume":"30 5","pages":"1996-2010"},"PeriodicalIF":15.9,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145310783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evolutionary Hypergraph Learning: A Multiobjective Influence Maximization Approach Considering Seed Selection Costs 进化超图学习：考虑种子选择成本的多目标影响最大化方法

IF 15.9 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2026-10-01 Epub Date: 2025-06-09 DOI: 10.1109/TEVC.2025.3577643

Xilong Qu;Wenbin Pei;Qianshi Wang;Yingchao Yang;Bing Xue;Mengjie Zhang;Qiang Zhang

{"title":"Evolutionary Hypergraph Learning: A Multiobjective Influence Maximization Approach Considering Seed Selection Costs","authors":"Xilong Qu;Wenbin Pei;Qianshi Wang;Yingchao Yang;Bing Xue;Mengjie Zhang;Qiang Zhang","doi":"10.1109/TEVC.2025.3577643","DOIUrl":"10.1109/TEVC.2025.3577643","url":null,"abstract":"Influence maximization (IM) has been extensively studied due to its wide range of applications in scenarios, such as transportation networks. In existing studies, the IM problem is primarily modeled by graphs, aiming to maximize influence spread only. However, graph-based modeling struggles to capture higher-order relationships between nodes. This becomes even more serious if seed selection costs are limited. Due to the strong coupling of high-order relationships between nodes and the complexity of propagation dynamics in a hypergraph, balancing the tradeoff between the influence spread and seed selection costs remains a significant challenge. In this article, we propose an evolutionary multiobjective approach to handling this challenge. The IM problem is formulated as a multiobjective optimization problem, where a low-complexity objective function is designed to optimize the influence spread, effectively leveraging the structural information of two-hop neighboring nodes to approximate the collective influence of the seed set. A novel population initialization method and mutation operator are proposed to accelerate the convergence speed while maintaining population diversity to prevent solutions from being trapped in local optima. Experiments on both synthetic and real-world hypergraphs demonstrate that the proposed method can obtain widely distributed nondominated solutions, offering diverse options to balance the influence spread and seed selection costs.","PeriodicalId":13206,"journal":{"name":"IEEE Transactions on Evolutionary Computation","volume":"30 5","pages":"1777-1790"},"PeriodicalIF":15.9,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144252139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Requirement-Driven Adaptive Differential Evolution for Many-Task Optimization 需求驱动的多任务优化自适应差分进化

IF 15.9 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2026-10-01 Epub Date: 2025-11-19 DOI: 10.1109/TEVC.2025.3634724

Zhi-Fan Tang;Zhi-Hui Zhan;Yun Li;Jun Zhang

{"title":"Requirement-Driven Adaptive Differential Evolution for Many-Task Optimization","authors":"Zhi-Fan Tang;Zhi-Hui Zhan;Yun Li;Jun Zhang","doi":"10.1109/TEVC.2025.3634724","DOIUrl":"10.1109/TEVC.2025.3634724","url":null,"abstract":"The major challenge in evolutionary many-task optimization (EMaTO) is to transfer knowledge among the tasks effectively. Moving on existing EMaTO algorithms, which mainly focus on what kind of knowledge to select and how to transfer the selected knowledge, this article focuses on a novel perspective to tackle the issue of when to transfer knowledge. This gives rise to a requirement-driven adaptive differential evolution (RADE) algorithm. Specifically, the RADE introduces a dual-scale evolutionary state estimator (ESE) to evaluate the state of evolution from both temporal and spatial scales, obtaining the real optimization requirement of the population and thus adaptively controlling the timing of knowledge transfer. This way, RADE not only leads to efficiently dealing with the when issue, but also drives two adaptive strategies for knowledge selection and knowledge transfer, leading to more efficient dealing with the what and the how issues, respectively. In particular, a requirement-driven adaptive knowledge selection strategy merges the evolutionary state similarity and the population distribution similarity for enhancing the selection of relevant source knowledge. Then, a requirement-driven adaptive knowledge transfer strategy is executed based on adaptive parameters and operators, thus significantly improving the efficiency of knowledge transfer. Experimental results on the CEC19 and WCCI22 benchmarks show that RADE outperforms the compared state-of-the-art EMaTO algorithms in both effectiveness and efficiency.","PeriodicalId":13206,"journal":{"name":"IEEE Transactions on Evolutionary Computation","volume":"30 5","pages":"2068-2081"},"PeriodicalIF":15.9,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145553455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0