IEEE Transactions on Evolutionary Computation最新文献_第10页

Branching Deep Q-Network Enhanced Memetic Algorithm for Skilled-Worker Constrained Flexible Job Shop Scheduling Problem 技术工人约束下柔性作业车间调度问题的分支深度q -网络增强模因算法

IF 14.3 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2026-08-10 DOI: 10.1109/tevc.2026.3722402

Chi Zhang, Quanke Pan, Ling Wang, Zhonghua Miao, Weimin Li

引用次数: 0

Physical Adversarial Laser Spot Attacks based on Evolutionary Competitive Multiobjective Multitasking Optimization 基于进化竞争多目标多任务优化的物理对抗性激光光斑攻击

IF 14.3 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2026-08-10 DOI: 10.1109/tevc.2026.3722369

Hao Li, Xilin Ren, Maoguo Gong, Yue Wu, Lining Xing

引用次数: 0

Illuminating Heuristic Design: Language-Model-Driven Quality-Diversity Evolution for Combinatorial Optimization 启发式设计：语言模型驱动的组合优化质量-多样性进化

IF 14.3 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2026-08-10 DOI: 10.1109/tevc.2026.3721299

Shikun Chen, Yangguang Liu

引用次数: 0

Bio-Inspired Neural Network for Energy-Aware Complete Coverage Path Planning of Reconfigurable Robot 基于仿生神经网络的可重构机器人能量感知全覆盖路径规划

IF 14.3 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2026-08-10 DOI: 10.1109/tevc.2026.3721909

Cong Hien Dinh, Huynh Van Van, Lim Yi, Ash Yaw Sang Wan, Mohan Rajesh Elara, Anh Vu Le

引用次数: 0

AutoPSO: A Meta-Framework for Automated Particle Swarm Optimization 自动粒子群优化的元框架

IF 14.3 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2026-08-03 DOI: 10.1109/tevc.2026.3718908

Xinmeng Yu, Jiaxin Gao, Jianguo Zhang, Dongmei Jiang, Ran Cheng

引用次数: 0

Lamarckian Inheritance Improves Robot Evolution in Dynamic Environments 拉马克遗传改进动态环境下机器人的进化

IF 15.9 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2026-08-01 Epub Date: 2025-10-08 DOI: 10.1109/TEVC.2025.3619278

Jie Luo;Karine Miras;Carlo Longhi;Oliver Weissl;Agoston E. Eiben

{"title":"Lamarckian Inheritance Improves Robot Evolution in Dynamic Environments","authors":"Jie Luo;Karine Miras;Carlo Longhi;Oliver Weissl;Agoston E. Eiben","doi":"10.1109/TEVC.2025.3619278","DOIUrl":"10.1109/TEVC.2025.3619278","url":null,"abstract":"Nature-inspired methods, such as evolutionary computing and lifetime learning, have shown great promise in advancing autonomous robot design. However, the integration of evolution and learning for the joint optimization of robot morphologies and controllers remains underexplored, particularly in dynamic environments. This article addresses this gap by investigating the effectiveness of Lamarckian inheritance (a mechanism that allows learned traits to be encoded into the genotype and passed to offspring) in improving robot evolution in nonstationary environments. We compare a Lamarckian system with a traditional Darwinian system, where learned traits are not inherited. Using simulated modular robots in six distinct environmental setups, we analyze the fitness progression, learning ability, and parent-offspring similarity within both systems. Our results demonstrate that the Lamarckian system consistently outperforms the Darwinian system, achieving up to 33% higher fitness in the most challenging conditions. The Lamarckian system also recovers more quickly from environmental changes, showing immediate fitness gains when shifting to complex terrains, whereas the Darwinian system adapts more slowly. Real-world tests validate the robustness of the Lamarckian approach, as the top-performing robots evolved in the most challenging environment exhibit the smallest reality gap. These findings highlight the potential of Lamarckian inheritance as a powerful tool for engineering adaptive robotic systems capable of maintaining high performance in dynamic environments.","PeriodicalId":13206,"journal":{"name":"IEEE Transactions on Evolutionary Computation","volume":"30 4","pages":"1495-1504"},"PeriodicalIF":15.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11196059","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145247109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evolutionary Contribution and Problem Heuristic Information Ensemble-Based Resource Allocation for Cooperative Coevolution 基于问题启发式信息集成的协同进化资源分配

IF 15.9 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2026-08-01 Epub Date: 2025-11-05 DOI: 10.1109/TEVC.2025.3629151

Dong Liu;Ming-Yuan Lu;Qiang Yang;Wei-Neng Chen;Ya-Hui Jia;Jian-Yu Li;Tao Li;Yuan-Yuan Ma;Jun Zhang

{"title":"Evolutionary Contribution and Problem Heuristic Information Ensemble-Based Resource Allocation for Cooperative Coevolution","authors":"Dong Liu;Ming-Yuan Lu;Qiang Yang;Wei-Neng Chen;Ya-Hui Jia;Jian-Yu Li;Tao Li;Yuan-Yuan Ma;Jun Zhang","doi":"10.1109/TEVC.2025.3629151","DOIUrl":"10.1109/TEVC.2025.3629151","url":null,"abstract":"This article proposes an evolutionary contribution and problem heuristic information ensemble-based computing resource allocation (CRA) scheme for cooperative co-evolutionary algorithms (CCEAs). For problem heuristic information (HI), this article assembles the correlation sensitivity of variables in each subproblem and the dimension ratio of this subproblem; for evolutionary contribution (EC), this article assembles the historical and the current ECs of each subproblem. By assembling these two crucial factors, the devised method computes the selection probability of each subproblem and then randomly picks one subproblem by the roulette wheel selection strategy to undergo optimization in each iteration. In this way, computing resources are preferentially allocated to those subproblems with high complexity manifested by the problem HI and high fitness improvement reflected by the EC. With this method, CCEAs expectedly fully utilize the computing resources to achieve satisfactory performance in addressing large-scale optimization problems (LSOPs). By combining the devised method with six latest decomposition methods along with two evolutionary optimizers, this article has conducted experiments to compare it with seven state-of-the-art CRA methods on two popular suites of LSOPs. Experimental results have proved that the devised method outperforms the seven compared methods in helping CCEAs achieve better performance.","PeriodicalId":13206,"journal":{"name":"IEEE Transactions on Evolutionary Computation","volume":"30 4","pages":"1729-1746"},"PeriodicalIF":15.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145447362","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

An Evolutionary Approach for the Computation of ϵ-Locally Optimal Solutions for Multiobjective Multimodal Optimization 多目标多模态优化中∈-局部最优解的进化计算方法

IF 15.9 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2026-08-01 Epub Date: 2025-11-26 DOI: 10.1109/TEVC.2025.3637276

Carlos Hernández;Angel E. Rodriguez-Fernandez;Lennart Schäpermeier;Oliver Cuate;Heike Trautmann;Oliver Schütze

{"title":"An Evolutionary Approach for the Computation of ϵ-Locally Optimal Solutions for Multiobjective Multimodal Optimization","authors":"Carlos Hernández;Angel E. Rodriguez-Fernandez;Lennart Schäpermeier;Oliver Cuate;Heike Trautmann;Oliver Schütze","doi":"10.1109/TEVC.2025.3637276","DOIUrl":"10.1109/TEVC.2025.3637276","url":null,"abstract":"In this article, we address the problem of efficiently computing finite-size approximations of the set of <inline-formula> <tex-math>$epsilon $ </tex-math></inline-formula>-locally optimal solutions of a given multiobjective optimization problem (MOP). Such sets are in particular interesting in the context of multiobjective multimodal optimization (MMO). To this end, we first propose a bounded archiver, <inline-formula> <tex-math>$ArchiveUpdateL_{Q,epsilon }B$ </tex-math></inline-formula>, that is, a modification of a previously proposed unbounded archiver. These archivers can be used as external archivers to in principle any multiobjective evolutionary algorithm (MOEA). In order to reduce the computational cost compared to such archive equipped MOEAs, we propose, in a next step <inline-formula> <tex-math>$L_{Q,epsilon }$ </tex-math></inline-formula>MOEA. This evolutionary algorithm directly uses <inline-formula> <tex-math>$ArchiveUpdateL_{Q,epsilon }B$ </tex-math></inline-formula> for the selection process and hence does not need an external archive for the computation of <inline-formula> <tex-math>$epsilon $ </tex-math></inline-formula>-locally optimal solutions. We further propose a hybrid of <inline-formula> <tex-math>$L_{Q,epsilon }$ </tex-math></inline-formula>MOEA with a multiobjective continuation method, which significantly improves the accuracy of the obtained solutions in case the gradient information is at hand. Finally, we show some numerical results that demonstrate the benefit of both the bounded archiver and the new MOEAs.","PeriodicalId":13206,"journal":{"name":"IEEE Transactions on Evolutionary Computation","volume":"30 4","pages":"1747-1760"},"PeriodicalIF":15.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145609252","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

ES-GP: An Ensemble Surrogate-Assisted Genetic Programming Approach to Image Classification 一种集成代理辅助遗传规划图像分类方法

IF 15.9 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2026-08-01 Epub Date: 2025-03-14 DOI: 10.1109/TEVC.2025.3569832

Qinglan Fan;Yunfeng Zhang;Xunxiang Yao;Ying Bi;Bing Xue;Mengjie Zhang

{"title":"ES-GP: An Ensemble Surrogate-Assisted Genetic Programming Approach to Image Classification","authors":"Qinglan Fan;Yunfeng Zhang;Xunxiang Yao;Ying Bi;Bing Xue;Mengjie Zhang","doi":"10.1109/TEVC.2025.3569832","DOIUrl":"10.1109/TEVC.2025.3569832","url":null,"abstract":"Genetic programming (GP) is a promising evolutionary machine learning technique for image classification, known for its ability to evolve flexible, effective, and interpretable models. However, the high computational cost of fitness evaluations in evolutionary learning restricts its practical applications. While Surrogate models offer efficient approximations for costly fitness evaluations, their application in GP-based image classification remains in its early stages, facing challenges such as handing flexible tree-based representations with variable lengths, designing an effective surrogate, and the limited performance of a single surrogate across various image classification tasks. To address these issues, this article proposes an ensemble surrogate-assisted GP approach to image classification. The new approach constructs one global surrogate model to explore broad areas and three local surrogate models within specific subspaces to exploit local regions, enabling more accurate predictions of GP individuals’ fitness. Moreover, a dynamic weighting strategy is developed to assign different weights to the base surrogate models in the ensemble, improving prediction accuracy. In addition, the proposed approach refines the surrogate training set construction method, previously limited to single-tree GP, enabling it to accelerate both single-tree and multitree GP for image classification. Experimental results on five datasets of varying difficulty demonstrate that the new ensemble surrogate method significantly reduces the number of expensive fitness evaluations of both single-tree and multitree GP-based image classification methods while achieving competitive performance. The comparisons with other state-of-the-art methods also confirm its effectiveness.","PeriodicalId":13206,"journal":{"name":"IEEE Transactions on Evolutionary Computation","volume":"30 4","pages":"1520-1533"},"PeriodicalIF":15.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143979605","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

Large-Scale Multimodal Multiobjective Optimization Based on Multiview Diversity Enhancement Mechanism 基于多视角多样性增强机制的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 : 2026-08-01 Epub Date: 2025-07-30 DOI: 10.1109/TEVC.2025.3594189

Tianzi Zheng;Jianchang Liu;Yaochu Jin;Xiangyu Wang;Yuanchao Liu

{"title":"Large-Scale Multimodal Multiobjective Optimization Based on Multiview Diversity Enhancement Mechanism","authors":"Tianzi Zheng;Jianchang Liu;Yaochu Jin;Xiangyu Wang;Yuanchao Liu","doi":"10.1109/TEVC.2025.3594189","DOIUrl":"10.1109/TEVC.2025.3594189","url":null,"abstract":"Large-scale multimodal multiobjective optimization problem with sparse Pareto optimal solutions pose a significant challenge in the field of optimization, primarily stemming from the multimodality characteristic, the curse of dimensionality, and the uncertainty of sparse solutions. In this work, we propose a sparse large-scale multimodal multiobjective evolutionary algorithm (MVDE-MMEA) to solve such complex tasks. In multiview diversity enhanced mechanism multimodal multiobjective evolutionary algorithm (MVDE-MMEA), a multiview diversity enhancement mechanism is designed to improve the exploration ability of the algorithm across the entire decision space. The diversity mechanism seamlessly transits from a global perspective to a local one as the population evolves, which contributes to an effective convergence toward multiple Pareto optimal set in large-scale decision space. Furthermore, a clustering method is proposed to divide the population into several niches, guided by the shared characteristics among candidates. In this way, the algorithm shifts its focus from exploration in the whole search space to exploitation in local regions. Experimental studies are conducted on eight benchmark test problems and 12 feature selection problems. The comparative results with the state-of-the-art algorithms demonstrate the superiority of MVDE-MMEA.","PeriodicalId":13206,"journal":{"name":"IEEE Transactions on Evolutionary Computation","volume":"30 4","pages":"1630-1644"},"PeriodicalIF":15.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144747503","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