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Learning to Expand/Contract Pareto Sets in Dynamic Multi-Objective Optimization With a Changing Number of Objectives IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2024-03-14 Gan Ruan, Leandro L. Minku, Stefan Menzel, Bernhard Sendhoff, Xin Yao
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Bayesian Optimisation for Quality Diversity Search With Coupled Descriptor Functions IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2024-03-13 Paul Kent, Adam Gaier, Jean-Baptiste Mouret, Juergen Branke
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Competitive Multitasking for Computational Resource Allocation in Evolutionary Constrained Multi-Objective Optimization IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2024-03-13 Xiaoliang Chu, Fei Ming, Wenyin Gong
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Multi-Objective Mixed-Integer Quadratic Models: A Study on Mathematical Programming and Evolutionary Computation IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2024-03-11 Ofer M. Shir, Michael Emmerich
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Learning to Preselection: A Filter-Based Performance Predictor for Multiobjective Feature Selection in Classification IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2024-03-06 Ruwang Jiao, Bing Xue, Mengjie Zhang
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Improved Evolutionary Multitasking Optimization Algorithm With Similarity Evaluation of Search Behavior IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2024-03-05 Xiaolong Wu, Wei Wang, Tengfei Zhang, Honggui Han, Junfei Qiao
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Analysis of Multi-Objective Evolutionary Algorithms On Fitness Function With Time-Linkage Property IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2024-02-29 Tianyi Yang, Yuren Zhou
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A Surrogate-Assisted Evolutionary Framework for Expensive Multitask Optimization Problems IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2024-02-28 Shenglian Tan, Yong Wang, Guangyong Sun, Tong Pang, Ke Tang
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Protein Structure Prediction Using A New Optimization-Based Evolutionary and Explainable Artificial Intelligence Approach IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2024-02-21 Jun Hong, Zhi-Hui Zhan, Langchong He, Zongben Xu, Jun Zhang
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MOEA/D With Spatial-Temporal Topological Tensor Prediction for Evolutionary Dynamic Multiobjective Optimization IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2024-02-20 Xianpeng Wang, Yumeng Zhao, Lixin Tang, Xin Yao
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Evolutionary Trainer-Based Deep Q-Network for Dynamic Flexible Job Shop Scheduling IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2024-02-19 Yun Liu, Fangfang Zhang, Yanan Sun, Mengjie Zhang
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Characterization of Constrained Continuous Multiobjective Optimization Problems: A Performance Space Perspective IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2024-02-16 Aljoša Vodopija, Tea Tušar, Bogdan Filipič
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A Cooperative Ant Colony System for Multiobjective Multirobot Task Allocation With Precedence Constraints IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2024-02-15 Tong Qian, Xiao-Fang Liu, Yongchun Fang
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A Bidirectional Differential Evolution Based Unknown Cyberattack Detection System IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2024-02-13 Hanyuan Huang, Tao Li, Beibei Li, Wenhao Wang, Yanan Sun
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Machine Learning Assisted Multiobjective Evolutionary Algorithm for Routing and Packing IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2024-02-06 Fei Liu, Qingfu Zhang, Qingling Zhu, Xialiang Tong, Mingxuan Yuan
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A Two-Individual Evolutionary Algorithm for Cumulative Capacitated Vehicle Routing With Single and Multiple Depots IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2024-02-05 Yuji Zou, Jin-Kao Hao, Qinghua Wu
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A Classifier-Ensemble-Based Surrogate-Assisted Evolutionary Algorithm for Distributed Data-Driven Optimization IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2024-02-01 Xiao-Qi Guo, Feng-Feng Wei, Jun Zhang, Wei-Neng Chen
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IEEE Transactions on Evolutionary Computation Information for Authors IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2024-01-30
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IEEE Computational Intelligence Society Information IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2024-01-30
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IEEE Transactions on Evolutionary Computation Publication Information IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2024-01-30
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A Decomposition-Based Evolutionary Algorithm With Clustering and Hierarchical Estimation for Multi-Objective Fuzzy Flexible Jobshop Scheduling IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2024-01-26 Xuwei Zhang, Shixin Liu, Ziyan Zhao, Shengxiang Yang
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Decoupling Constraint: Task Clone-Based Multi-Tasking Optimization for Constrained Multi-Objective Optimization IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2024-01-26 Genghui Li, Zhenkun Wang, Weifeng Gao, Ling Wang
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On Cooperative Coevolution and Global Crossover IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2024-01-18 Larry Bull, Haixia Liu
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Knowledge Structure Preserving-Based Evolutionary Many-Task Optimization IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2024-01-18 Yi Jiang, Zhi-Hui Zhan, Kay Chen Tan, Sam Kwong, Jun Zhang
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A Flexible Ranking-Based Competitive Swarm Optimizer for Large-Scale Continuous Multi-Objective Optimization IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2024-01-17 Xiangzhou Gao, Shenmin Song, Hu Zhang, Zhenkun Wang
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A Bi-Learning Evolutionary Algorithm for Transportation-Constrained and Distributed Energy-Efficient Flexible Scheduling IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2024-01-16 Zixiao Pan, Ling Wang, Jingjing Wang, Qingfu Zhang
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Multiobjective Multitask Optimization With Multiple Knowledge Types and Transfer Adaptation IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2024-01-12 Yanchi Li, Wenyin Gong
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Finding the Set of Nearly Optimal Solutions of a Multi-Objective Optimization Problem IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2024-01-12 Oliver Schütze, Angel E. Rodriguez-Fernandez, Carlos Segura, Carlos Hernández
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Toward Evolving Dispatching Rules With Flow Control Operations By Grammar-Guided Linear Genetic Programming IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2024-01-12 Zhixing Huang, Yi Mei, Fangfang Zhang, Mengjie Zhang
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Tree-Based Codification in Neural Architecture Search for Medical Image Segmentation IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2024-01-11 José-Antonio Fuentes-Tomás, Efrén Mezura-Montes, Héctor-Gabriel Acosta-Mesa, Aldo Márquez-Grajales
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MedNAS: Multi-Scale Training-Free Neural Architecture Search for Medical Image Analysis IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2024-01-11 Yan Wang, Liangli Zhen, Jianwei Zhang, Miqing Li, Lei Zhang, Zizhou Wang, Yangqin Feng, Yu Xue, Xiao Wang, Zheng Chen, Tao Luo, Rich Siow Mong Goh, Yong Liu
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Enhancing Fitness Evaluation in Genetic Algorithm-Based Architecture Search for AI-Aided Financial Regulation IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2024-01-10 Jian Feng, Yajie He, Yuhan Pan, Zhipeng Zhou, Si Chen, Wei Gong
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Speeding-Up Evolutionary Algorithms to Solve Black-Box Optimization Problems IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2024-01-10 Judith Echevarrieta, Etor Arza, Aritz Pérez
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ExTrEMO: Transfer Evolutionary Multiobjective Optimization With Proof of Faster Convergence IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2024-01-02 Jiao Liu, Abhishek Gupta, Chinchun Ooi, Yew-Soon Ong
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Evolutionary Multitask Optimization With Lower Confidence Bound-Based Solution Selection Strategy IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2024-01-02 Zhenzhong Wang, Lulu Cao, Liang Feng, Min Jiang, Kay Chen Tan
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Evolutionary Multi-Objective Optimisation for Large-Scale Portfolio Selection With Both Random and Uncertain Returns IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2024-01-01 Weilong Liu, Yong Zhang, Kailong Liu, Barry Quinn, Xingyu Yang, Qiao Peng
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Guest Editorial Special Issue on Multiobjective Evolutionary Optimization in Machine Learning IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2023-08-01 Uwe Aickelin, Hadi Akbarzadeh Khorshidi, Rong Qu, Hadi Charkhgard
We are very pleased to introduce this special issue on multiobjective evolutionary optimization for machine learning (MOML). Optimization is at the heart of many machine-learning techniques. However, there is still room to exploit optimization in machine learning. Every machine-learning technique has hyperparameters that can be tuned using evolutionary computation and optimization, considering normally
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Guest Editorial Special Issue on Large-Scale Evolutionary Multiobjective Optimization and Its Practical Applications IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2023-05-30 Xingyi Zhang, Ran Cheng, Yaochu Jin, Bernhard Sendhoff
Complex optimization problems with hundreds or even thousands of decision variables and dozens of conflicting objectives are not uncommon in the real world. In the past five years, increased research efforts have been dedicated to large-scale multiobjective optimization problems (LSMOPs) by using a variety of search strategies, including variable grouping, variable analysis, problem transformation
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A Multipopulation Evolutionary Algorithm Using New Cooperative Mechanism for Solving Multiobjective Problems With Multiconstraint IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2023-03-22 Juan Zou, Ruiqing Sun, Yuan Liu, Yaru Hu, Shengxiang Yang, Jinhua Zheng, Ke Li
In science and engineering, multiobjective optimization problems (MOPs) usually contain multiple complex constraints, which poses a significant challenge in obtaining the optimal solution. This article aims to solve the challenges brought by multiple complex constraints. First, this article analyzes the relationship between single-constrained Pareto front (SCPF) and their common Pareto front (PF) subconstrained
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An Interactive Knowledge-Based Multiobjective Evolutionary Algorithm Framework for Practical Optimization Problems IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2023-03-20 Abhiroop Ghosh, Kalyanmoy Deb, Erik Goodman, Ronald Averill
Experienced users often have useful knowledge and intuition in solving real-world optimization problems. User knowledge can be formulated as intervariable relationships to assist an optimization algorithm in finding good solutions faster. Such intervariable interactions can also be automatically learned from high-performing solutions discovered at intermediate iterations in an optimization run—a process
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Multitask Particle Swarm Optimization With Heterogeneous Domain Adaptation IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2023-03-17 Honggui Han, Xing Bai, Ying Hou, Junfei Qiao
The main goal of multitask optimization (MTO) is the parallel optimization of multiple different tasks. However, since different tasks in the MTO problem usually have heterogeneous characteristics, it is difficult to realize the positive knowledge transfer among tasks, resulting in poor convergence. To cope with this problem, a multitask particle swarm optimization (MTPSO) with a heterogeneous domain
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Higher Order Knowledge Transfer for Dynamic Community Detection With Great Changes IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2023-03-15 Huixin Ma, Kai Wu, Handing Wang, Jing Liu
Network structure evolves with time in the real world, and the discovery of changing communities in dynamic networks is an important research topic that poses challenging tasks. Most existing methods assume that no significant change occurs; namely, the difference between adjacent snapshots is slight. However, great change exists in the real world usually. The great change in the network will result
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Bi-Level Multiobjective Evolutionary Learning: A Case Study on Multitask Graph Neural Topology Search IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2023-03-10 Chao Wang, Licheng Jiao, Jiaxuan Zhao, Lingling Li, Xu Liu, Fang Liu, Shuyuan Yang
The construction of machine learning models involves many bi-level multiobjective optimization problems (BL-MOPs), where upper-level (UL) candidate solutions must be evaluated via training weights of a model in the lower level (LL). Due to the Pareto optimality of subproblems and the complex dependency across UL solutions and LL weights, a UL solution is feasible if and only if the LL weight is Pareto
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Survey on Genetic Programming and Machine Learning Techniques for Heuristic Design in Job Shop Scheduling IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2023-03-10 Fangfang Zhang, Yi Mei, Su Nguyen, Mengjie Zhang
Job shop scheduling (JSS) is a process of optimizing the use of limited resources to improve the production efficiency. JSS has a wide range of applications, such as order picking in the warehouse and vaccine delivery scheduling under a pandemic. In real-world applications, the production environment is often complex due to dynamic events, such as job arrivals over time and machine breakdown. Scheduling
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A Mahalanobis Distance-Based Approach for Dynamic Multiobjective Optimization With Stochastic Changes IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2023-03-08 Yaru Hu, Jinhua Zheng, Shouyong Jiang, Shengxiang Yang, Juan Zou, Rui Wang
In recent years, researchers have made significant progress in handling dynamic multiobjective optimization problems (DMOPs), particularly for environmental changes with predictable characteristics. However, little attention has been paid to DMOPs with stochastic changes. It may be difficult for existing dynamic multiobjective evolutionary algorithms (DMOEAs) to effectively handle this kind of DMOPs
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An Evolutionary Multitasking Algorithm With Multiple Filtering for High-Dimensional Feature Selection IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2023-03-08 Lingjie Li, Manlin Xuan, Qiuzhen Lin, Min Jiang, Zhong Ming, Kay Chen Tan
Recently, evolutionary multitasking (EMT) has been successfully used in the field of high-dimensional classification. However, the generation of multiple tasks in the existing EMT-based feature selection (FS) methods is relatively simple, using only the Relief- ${F}$ method to collect related features with similar importance into one task, which cannot provide more diversified tasks for knowledge transfer
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Neural Architecture Search Based on a Multi-Objective Evolutionary Algorithm With Probability Stack IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2023-03-06 Yu Xue, Chen Chen, Adam Słowik
With the emergence of deep neural networks, many research fields, such as image classification, object detection, speech recognition, natural language processing, machine translation, and automatic driving, have made major breakthroughs in technology and the research achievements have been successfully applied in many real-life applications. Combining evolutionary computation and neural architecture
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Identifying Pareto Fronts Reliably Using a Multistage Reference-Vector-Based Framework IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2023-03-01 Kalyanmoy Deb, Claudio Lucio do Val Lopes, Flávio Vinícius Cruzeiro Martins, Elizabeth Fialho Wanner
Evolutionary multiobjective and many-objective optimization (EMO and EMaO) algorithms are increasingly used to identify the true shape and location of the Pareto-optimal front using a few representative well-converged and well-distributed solutions. The reason for their popularity is due to their ability to provide a better understanding of objective relationships for optimal solutions, and also to
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A Survey on Learnable Evolutionary Algorithms for Scalable Multiobjective Optimization IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2023-02-28 Songbai Liu, Qiuzhen Lin, Jianqiang Li, Kay Chen Tan
Recent decades have witnessed great advancements in multiobjective evolutionary algorithms (MOEAs) for multiobjective optimization problems (MOPs). However, these progressively improved MOEAs have not necessarily been equipped with scalable and learnable problem-solving strategies for new and grand challenges brought by the scaling-up MOPs with continuously increasing complexity from diverse aspects
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History-Guided Hill Exploration for Evolutionary Computation IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2023-02-28 Junchen Wang, Changhe Li, Sanyou Zeng, Shengxiang Yang
Although evolutionary computing (EC) methods are stochastic optimization methods, it is usually difficult to find the global optimum by restarting the methods when the population converges to a local optimum. A major reason is that many optimization problems have basins of attraction (BoAs) that differ widely in shape and size, and the population always prefers to converge toward BoAs that are easy
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To Trust or Not to Trust: Evolutionary Dynamics of an Asymmetric N-Player Trust Game IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2023-02-13 Ik Soo Lim, Naoki Masuda
Trusting others and reciprocating the received trust with trustworthy actions are fundaments of economic and social interactions. The trust game (TG) is widely used for studying trust and trustworthiness and entails a sequential interaction between two players, an investor and a trustee. It requires at least two strategies or options for an investor (e.g., to trust versus not to trust a trustee). According
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Surrogate-Assisted Environmental Selection for Fast Hypervolume-Based Many-Objective Optimization IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2023-02-09 Shulei Liu, Handing Wang, Wen Yao, Wei Peng
Hypervolume (HV)-based evolutionary algorithms have been widely used to handle many-objective optimization problems. In such algorithms, HV-based environmental selection (HVES), which aims at selecting a subpopulation with the maximal HV from the current population, plays a crucial role in guiding evolution. However, the computation time of HV increases exponentially with the number of objectives,
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Process Knowledge-Guided Autonomous Evolutionary Optimization for Constrained Multiobjective Problems IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2023-02-07 Mingcheng Zuo, Dunwei Gong, Yan Wang, Xianming Ye, Bo Zeng, Fanlin Meng
Various real-world problems can be attributed to constrained multiobjective optimization problems (CMOPs). Although there are various solution methods, it is still very challenging to automatically select efficient solving strategies for CMOPs. Given this, a process knowledge-guided constrained multiobjective autonomous evolutionary optimization method is proposed. First, the effects of different solving
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Obfuscating Community Structure in Complex Network With Evolutionary Divide-and-Conquer Strategy IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2023-02-03 Jie Zhao, Kang Hao Cheong
As the number of social network users grows exponentially with increasingly complex profiles, community detection algorithms play a critical role in user portrait analysis. The associated privacy concerns, however, have not sufficiently received the attention that it deserves. In this work, we investigate methods for obfuscating the original community structure by modifying a small number of connections
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Guest Editorial Special Issue on Evolutionary Computer Vision IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2023-01-30 Gustavo Olague, Mario Köppen, Oscar Cordón
Evolutionary Computer Vision (ECV) is at the intersection of two major research fields of artificial intelligence: 1) computer vision (CV) and 2) evolutionary computation (EC). This special issue brings an overview of state-of-the-art contributions to the latest research and development in the discipline. CV includes methods for acquiring, processing, analyzing, and understanding images. The aim is
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SFE: A Simple, Fast, and Efficient Feature Selection Algorithm for High-Dimensional Data IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2023-01-23 Behrouz Ahadzadeh, Moloud Abdar, Fatemeh Safara, Abbas Khosravi, Mohammad Bagher Menhaj, Ponnuthurai Nagaratnam Suganthan
In this article, a new feature selection (FS) algorithm, called simple, fast, and efficient (SFE), is proposed for high-dimensional datasets. The SFE algorithm performs its search process using a search agent and two operators: 1) nonselection and 2) selection. It comprises two phases: 1) exploration and 2) exploitation. In the exploration phase, the nonselection operator performs a global search in
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Multiobjective Differential Evolution Algorithm Balancing Multiple Stakeholders for Low-Carbon Order Scheduling in E-Waste Recycling IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2023-01-17 Ying Hou, Yilin Wu, Honggui Han
Order scheduling is an important part of the e-waste recycling process, which can influence the quantity and efficiency of the recycling. With the sustainable development of e-waste recycling, low-carbon order scheduling becomes a significant and challenging reverse logistics scheduling problem. However, it is difficult to obtain an effective low-carbon order schedule considering the conflicting interests
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A Performance Indicator-Based Infill Criterion for Expensive Multi-/Many-Objective Optimization IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2023-01-17 Shufen Qin, Chaoli Sun, Qiqi Liu, Yaochu Jin
In surrogate-assisted multi-/many-objective evolutionary optimization, each solution normally has an approximated value on each objective, resulting in increased difficulties in selecting solutions for expensive objective evaluations due to complicated tradeoff between different objectives and accumulated uncertainty in the approximation of the objective functions. Thus, it is highly challenging to
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Interactive Evolutionary Multiobjective Optimization via Learning to Rank IEEE T. Evolut. Comput. (IF 14.3) Pub Date : 2023-01-11 Ke Li, Guiyu Lai, Xin Yao
In practical multicriterion decision making, it is cumbersome if a decision maker (DM) is asked to choose among a set of tradeoff alternatives covering the whole Pareto-optimal front. This is a paradox in conventional evolutionary multiobjective optimization (EMO) that always aim to achieve a well balance between convergence and diversity. In essence, the ultimate goal of multiobjective optimization