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Theoretical Analysis of Explicit Averaging and Novel Sign Averaging in Comparison-Based Search IEEE T. Evolut. Comput. (IF 11.7) Pub Date : 2024-09-20 Daiki Morinaga, Youhei Akimoto
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Automated Metaheuristic Algorithm Design With Autoregressive Learning IEEE T. Evolut. Comput. (IF 11.7) Pub Date : 2024-09-20 Qi Zhao, Tengfei Liu, Bai Yan, Qiqi Duan, Jian Yang, Yuhui Shi
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Using the Empirical Attainment Function for Analyzing Single-Objective Black-Box Optimization Algorithms IEEE T. Evolut. Comput. (IF 11.7) Pub Date : 2024-09-17 Manuel López-Ibáñez, Diederick Vermetten, Johann Dreo, Carola Doerr
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A Simple yet Effective Greedy Evolutionary Strategy for RNA Design IEEE T. Evolut. Comput. (IF 11.7) Pub Date : 2024-09-16 Nuria Lozano-García, Álvaro Rubio-Largo, José Maria Granado-Criado
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A Diversity-Enhanced Tri-Stage Framework for Constrained Multi-objective Optimization IEEE T. Evolut. Comput. (IF 11.7) Pub Date : 2024-09-13 Yubo Wang, Chengyu Hu, Fei Ming, Yanchi Li, Wenyin Gong, Liang Gao
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Transfer Task-Averaged Natural Gradient for Efficient Many-Task Optimization IEEE T. Evolut. Comput. (IF 11.7) Pub Date : 2024-09-13 Yanchi Li, Wenyin Gong, Qiong Gu
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An Automated and Interpretable Computer-Aided Approach for Skin Cancer Diagnosis Using Genetic Programming IEEE T. Evolut. Comput. (IF 11.7) Pub Date : 2024-09-12 Kunjie Yu, Jintao Lian, Ying Bi, Jing Liang, Bing Xue, Mengjie Zhang
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Finding ϵ-Locally Optimal Solutions for Multi-Objective Multimodal Optimization IEEE T. Evolut. Comput. (IF 11.7) Pub Date : 2024-09-11 Angel E. Rodriguez-Fernandez, Lennart Schäpermeier, Carlos Hernández, Pascal Kerschke, Heike Trautmann, Oliver Schütze
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Transforming Combinatorial Optimization Problems in Fourier Space: Consequences and Uses IEEE T. Evolut. Comput. (IF 11.7) Pub Date : 2024-09-10 Anne Elorza, Xabier Benavides, Josu Ceberio, Leticia Hernando, Jose A. Lozano
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A Memetic Algorithm for Vehicle Routing with Simultaneous Pickup and Delivery and Time Windows IEEE T. Evolut. Comput. (IF 11.7) Pub Date : 2024-09-09 Zhenyu Lei, Jin-Kao Hao
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Knee Detection in Bayesian Multi-Objective Optimization Using Thompson Sampling IEEE T. Evolut. Comput. (IF 11.7) Pub Date : 2024-09-06 Arash Heidari, Jixiang Qing, Sebastian Rojas Gonzalez, Juergen Branke, Tom Dhaene, Ivo Couckuyt
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A Divergence-Based Condition to Ensure Quantile Improvement in Black-Box Global Optimization IEEE T. Evolut. Comput. (IF 11.7) Pub Date : 2024-09-02 Thomas Guilmeau, Emilie Chouzenoux, Víctor Elvira
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The Runtime of Randomized Local Search on the Generalized Needle Problem IEEE T. Evolut. Comput. (IF 11.7) Pub Date : 2024-09-02 Benjamin Doerr, Andrew Kelley
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A Streaming Feature Selection Method Based on Dynamic Feature Clustering and Particle Swarm Optimization IEEE T. Evolut. Comput. (IF 11.7) Pub Date : 2024-08-29 Xianfang Song, Hao Ma, Yong Zhang, Dunwei Gong, Yinan Guo, Ying Hu
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Spatial-Temporal Knowledge Transfer for Dynamic Constrained Multiobjective Optimization IEEE T. Evolut. Comput. (IF 11.7) Pub Date : 2024-08-23 Zhenzhong Wang, Dejun Xu, Min Jiang, Kay Chen Tan
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An Iterated Greedy Algorithm With Reinforcement Learning for Distributed Hybrid FlowShop Problems With Job Merging IEEE T. Evolut. Comput. (IF 11.7) Pub Date : 2024-08-14 Xin-rui Tao, Quan-ke Pan, Liang Gao
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Exact Calculation of Inverted Generational Distance IEEE T. Evolut. Comput. (IF 11.7) Pub Date : 2024-08-14 Zihan Wang, Chunyun Xiao, Aimin Zhou
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Bridging Evolutionary Algorithms and Reinforcement Learning: A Comprehensive Survey on Hybrid Algorithms IEEE T. Evolut. Comput. (IF 11.7) Pub Date : 2024-08-14 Pengyi Li, Jianye Hao, Hongyao Tang, Xian Fu, Yan Zhen, Ke Tang
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An Adaptive Multi-Strategy Algorithm Based on Extent of Environmental Change for Dynamic Multiobjective Optimization IEEE T. Evolut. Comput. (IF 11.7) Pub Date : 2024-08-12 Yong Wang, Kuichao Li, Gai-Ge Wang
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TechRxiv: Share Your Preprint Research with the World! IEEE T. Evolut. Comput. (IF 11.7) Pub Date : 2024-04-01
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IEEE Computational Intelligence Society Information IEEE T. Evolut. Comput. (IF 11.7) Pub Date : 2024-04-01
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Multi-Tree Genetic Programming for Learning Color and Multi-Scale Features in Image Classification IEEE T. Evolut. Comput. (IF 11.7) Pub Date : 2024-04-01 Qinglan Fan, Ying Bi, Bing Xue, Mengjie Zhang
Data-efficient image classification, which focuses on achieving accurate classification performance with limited labeled data, has garnered significant attention. Genetic programming (GP) has achieved impressive progress in image classification, particularly in scenarios involving small amounts of labeled data. GP research typically focuses on designing tree-based model representations to learn useful
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Solving High-Dimensional Expensive Multiobjective Optimization Problems by Adaptive Decision Variable Grouping IEEE T. Evolut. Comput. (IF 11.7) Pub Date : 2024-03-29 Yingwei Li, Xiang Feng, Huiqun Yu
Plenty of decision variable grouping based algorithms have shown satisfactory performance in solving high-dimensional optimization problems. However, most of them are tailored for inexpensive optimization problems. Extending variable grouping method to expensive optimization problems poses many challenges. One of the greatest challenges is that most grouping approaches require additional function evaluations
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A Survey on Evolutionary Computation Based Drug Discovery IEEE T. Evolut. Comput. (IF 11.7) Pub Date : 2024-03-26 Qiyuan Yu, Qiuzhen Lin, Junkai Ji, Wei Zhou, Shan He, Zexuan Zhu, Kay Chen Tan
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Deep Reinforcement Learning Assisted Genetic Programming Ensemble Hyper-Heuristics for Dynamic Scheduling of Container Port Trucks IEEE T. Evolut. Comput. (IF 11.7) Pub Date : 2024-03-25 Xinan Chen, Ruibin Bai, Rong Qu, Jing Dong, Yaochu Jin
Efficient truck dispatching is crucial for optimizing container terminal operations within dynamic and complex scenarios. Despite good progress being made recently with more advanced uncertainty-handling techniques, existing approaches still have generalization issues and require considerable expertise and manual interventions in algorithm design. In this work, we present deep reinforcement learning-assisted
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Multi-Agent Swarm Optimization With Adaptive Internal and External Learning for Complex Consensus-Based Distributed Optimization IEEE T. Evolut. Comput. (IF 11.7) Pub Date : 2024-03-25 Tai-You Chen, Wei-Neng Chen, Feng-Feng Wei, Xiao-Min Hu, Jun Zhang
Distributed optimization has attracted lots of attention in recent years. Thanks to the intrinsic parallelism and great search capacity, evolutionary computation (EC) has the potential for black-box and non-convex distributed optimization. However, due to the decentralization of local objective functions, it is challenging to optimize the global objective function with efficient communication and guaranteed
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Learning-Aided Evolutionary Search and Selection for Scaling-up Constrained Multiobjective Optimization IEEE T. Evolut. Comput. (IF 11.7) Pub Date : 2024-03-21 Songbai Liu, Zeyi Wang, Qiuzhen Lin, Jianqiang Li, Kay Chen Tan
The existing constrained multiobjective evolutionary algorithms (CMOEAs) still have great room for improvement in balancing populations convergence, diversity and feasibility on complex constrained multiobjective optimization problems (CMOPs). Besides, their effectiveness deteriorates dramatically when facing the CMOPs with scaling-up objective space or search space. We are thus motivated to design
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Computationally Expensive High-Dimensional Multiobjective Optimization via Surrogate-Assisted Reformulation and Decomposition IEEE T. Evolut. Comput. (IF 11.7) Pub Date : 2024-03-21 Linqiang Pan, Jianqing Lin, Handing Wang, Cheng He, Kay Chen Tan, Yaochu Jin
In recent decades, various surrogate-assisted evolutionary algorithms (SAEAs) have been proposed to solve computationally expensive multiobjective optimization problems (EMOPs). Nevertheless, designing an SAEA to handle high-dimensional EMOPs and balance convergence, diversity, and computational complexity remains challenging. Here, we propose a two-phase SAEA (TP-SAEA), which follows the idea of convergence
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Benchmarking Derivative-Free Global Optimization Algorithms Under Limited Dimensions and Large Evaluation Budgets IEEE T. Evolut. Comput. (IF 11.7) Pub Date : 2024-03-20 Linas Stripinis, Jakub K暖dela, Remigijus Paulavi膷ius
This paper addresses the challenge of selecting the most suitable optimization algorithm by presenting a comprehensive computational comparison between stochastic and deterministic methods. The complexity of algorithm selection arises from the absence of a universal algorithm and the abundance of available options. Manual selection without comprehensive studies can lead to suboptimal or incorrect results
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Genetic Programming With Flexible Region Detection for Fine-Grained Image Classification IEEE T. Evolut. Comput. (IF 11.7) Pub Date : 2024-03-19 Qinyu Wang, Ying Bi, Bing Xue, Mengjie Zhang
Fine-grained image classification (FGIC) is an important computer vision task with many real-world applications. However, FGIC is challenging due to intra-class variations and inter-class similarities, especially when there is limited training data. To address these challenges, a new genetic programming approach with flexible region detection, GP-RD, is proposed for different FGIC tasks, i.e., flower
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A Syntactic Problem Solver Learning Landscape Structures for Clinical Scheduling IEEE T. Evolut. Comput. (IF 11.7) Pub Date : 2024-03-18 Keyao Wang, Bo Liu
This paper attempts to derive a mathematical formulation for real-practice clinical laboratory scheduling, and to present a syntactic problem solver by leveraging instances’ landscape structures to infer the most suitable search strategy. After formulating scheduling of medical tests as a distributed scheduling problem in heterogeneous, flexible job shop environment, we establish a mixed integer programming
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An Interval Multi-Objective Evolutionary Generation Algorithm for Product Design Change Plans in Uncertain Environments IEEE T. Evolut. Comput. (IF 11.7) Pub Date : 2024-03-18 Rui-zhao Zheng, Yong Zhang, Xiao-yan Sun, Dun-wei Gong, Xiao-zhi Gao
Design change is an important issue in complex product development projects. In a complex product with numerous parts (also known as components), the change of one key part may spread to other parts associated with it, generating a chain reaction throughout the entire project. Therefore, it is necessary to select a suitable change plan involving only fewer crucial parts in order to enhance the product’s
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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 11.7) 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 11.7) Pub Date : 2024-03-13 Paul Kent, Adam Gaier, Jean-Baptiste Mouret, Juergen Branke
Quality Diversity (QD) algorithms such as MAP-Elites are a class of optimisation techniques that attempt to find many high performing points that all behave differently according to a user-defined behavioural metric. In this paper we propose the Bayesian Optimisation of Elites (BOP-Elites) algorithm. Designed for problems with expensive black-box objective and behaviour functions, it is able to return
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Competitive Multitasking for Computational Resource Allocation in Evolutionary Constrained Multi-Objective Optimization IEEE T. Evolut. Comput. (IF 11.7) Pub Date : 2024-03-13 Xiaoliang Chu, Fei Ming, Wenyin Gong
Constrained multi-objective optimization problems (CMOPs) have multiple objective functions that need to be optimized and constraints need to be satisfied, making them difficult to solve. Based on the multitasking optimization, the optimization of the original CMOP can be transformed into multiple related sub-tasks. Existing multitasking-based constrained multi-objective optimization evolutionary algorithms
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Multi-Objective Mixed-Integer Quadratic Models: A Study on Mathematical Programming and Evolutionary Computation IEEE T. Evolut. Comput. (IF 11.7) Pub Date : 2024-03-11 Ofer M. Shir, Michael Emmerich
Within the current literature on multi-objective optimization, there is a scarcity of comparisons between equation-based white-box solvers to evolutionary black-box solvers. It is commonly held that when dealing with linear and quadratic models, equation-based deterministic solvers are generally the preferred choice. The present study aims at challenging this hypothesis, and we show that particularly
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Learning to Preselection: A Filter-Based Performance Predictor for Multiobjective Feature Selection in Classification IEEE T. Evolut. Comput. (IF 11.7) Pub Date : 2024-03-06 Ruwang Jiao, Bing Xue, Mengjie Zhang
Minimizing the classification error rate and the number of selected features are the two major objectives of feature selection, and they are often in conflict with each other, which is a multiobjective problem. Evolutionary algorithms have been widely used for multiobjective feature selection problems. Preselection in evolutionary algorithms is used to improve the sampling quality by selecting only
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Improved Evolutionary Multitasking Optimization Algorithm With Similarity Evaluation of Search Behavior IEEE T. Evolut. Comput. (IF 11.7) Pub Date : 2024-03-05 Xiaolong Wu, Wei Wang, Tengfei Zhang, Honggui Han, Junfei Qiao
Task similarity is a major requisite to trigger knowledge sharing in evolutionary multitasking optimization (EMTO). Unfortunately, most of the existing EMTO algorithms only focus on the similarity between population distributions of tasks, but ignore the search behavior of populations, which may degrade the performance of cross-task knowledge sharing. Motivated by this, an improved EMTO algorithm with
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Analysis of Multiobjective Evolutionary Algorithms on Fitness Function With Time-Linkage Property IEEE T. Evolut. Comput. (IF 11.7) Pub Date : 2024-02-29 Tianyi Yang, Yuren Zhou
The time-linkage property, which means that the optimization problem not only relies on the current solution but also on historical solutions, is common in real-world applications. Although the theoretical studies on multiobjective evolutionary algorithms (MOEAs) have been rapidly developed in decades, there exists no theoretical analyses for MOEAs on time-linkage problems. This letter aims to take
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A Surrogate-Assisted Evolutionary Framework for Expensive Multitask Optimization Problems IEEE T. Evolut. Comput. (IF 11.7) 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 11.7) Pub Date : 2024-02-21 Jun Hong, Zhi-Hui Zhan, Langchong He, Zongben Xu, Jun Zhang
Protein structure prediction (PSP) is an important scientific problem because it helps humans to understand how proteins perform their biological functions. This paper models the PSP problem as a multi-objective optimization problem with three fast and accurate knowledge-based energy functions. This way, using evolutionary computation (EC)-based artificial intelligence (AI) approach to solve this multi-objective
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MOEA/D With Spatial-Temporal Topological Tensor Prediction for Evolutionary Dynamic Multiobjective Optimization IEEE T. Evolut. Comput. (IF 11.7) Pub Date : 2024-02-20 Xianpeng Wang, Yumeng Zhao, Lixin Tang, Xin Yao
When solving dynamic multiobjective optimization problems, most evolutionary algorithms attempt to predict the initial population in a new environment by mining the relationships between solutions during historical environment changes. However, the complex relationships between solutions and the limited amount of available data often make it difficult to extract useful information efficiently, which
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Evolutionary Trainer-Based Deep Q-Network for Dynamic Flexible Job Shop Scheduling IEEE T. Evolut. Comput. (IF 11.7) Pub Date : 2024-02-19 Yun Liu, Fangfang Zhang, Yanan Sun, Mengjie Zhang
Dynamic flexible job shop scheduling (DFJSS) aims to achieve the optimal efficiency for production planning in the face of dynamic events. In practice, deep Q-network (DQN) algorithms have been intensively studied for solving various DFJSS problems. However, these algorithms often cause moving targets for the given job-shop state. This will inevitably lead to unstable training and severe deterioration
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Characterization of Constrained Continuous Multiobjective Optimization Problems: A Performance Space Perspective IEEE T. Evolut. Comput. (IF 11.7) Pub Date : 2024-02-16 Aljo拧a Vodopija, Tea Tu拧ar, Bogdan Filipi膷
Constrained multiobjective optimization has gained much interest in the past few years. However, constrained multiobjective optimization problems (CMOPs) are still unsatisfactorily understood. Consequently, the choice of adequate CMOPs for benchmarking is difficult and lacks a formal background. This paper takes a step towards addressing this issue by exploring CMOPs from a performance space perspective
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A Cooperative Ant Colony System for Multiobjective Multirobot Task Allocation With Precedence Constraints IEEE T. Evolut. Comput. (IF 11.7) Pub Date : 2024-02-15 Tong Qian, Xiao-Fang Liu, Yongchun Fang
In many real-world scenarios (e.g., product manufacturing), multiple heterogeneous robots cooperate to complete complex tasks with precedence constraints. In these heterogeneous multirobot systems, the multirobot task allocation problem is important and has attracted increasing attention. The problem usually involves multiple optimization objectives for decision making. However, existing approaches
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A Bidirectional Differential Evolution Based Unknown Cyberattack Detection System IEEE T. Evolut. Comput. (IF 11.7) 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 11.7) Pub Date : 2024-02-06 Fei Liu, Qingfu Zhang, Qingling Zhu, Xialiang Tong, Mingxuan Yuan
Many combinatorial multiobjective optimization problems involve very costly-to-evaluate objectives and constraints. It is very difficult, if not impossible, for traditional heuristics to solve these problems with an acceptable amount of computational time. In this paper, we show that offline machine learning can be very useful to assist multiobjective evolutionary algorithms to tackle this kind of
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A Two-Individual Evolutionary Algorithm for Cumulative Capacitated Vehicle Routing With Single and Multiple Depots IEEE T. Evolut. Comput. (IF 11.7) 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 11.7) Pub Date : 2024-02-01 Xiao-Qi Guo, Feng-Feng Wei, Jun Zhang, Wei-Neng Chen
Surrogate-assisted evolutionary algorithms (SAEAs) have achieved effective performance in solving complex data-driven optimization problems. In the Internet of Things environment, the data of many problems are collected and processed in distributed network nodes and cannot be transmitted. As each local node can only access and build surrogate models based on partial data, local models are usually not
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IEEE Transactions on Evolutionary Computation Information for Authors IEEE T. Evolut. Comput. (IF 11.7) Pub Date : 2024-01-30
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IEEE Computational Intelligence Society Information IEEE T. Evolut. Comput. (IF 11.7) Pub Date : 2024-01-30
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IEEE Transactions on Evolutionary Computation Publication Information IEEE T. Evolut. Comput. (IF 11.7) 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 11.7) Pub Date : 2024-01-26 Xuwei Zhang, Shixin Liu, Ziyan Zhao, Shengxiang Yang
As an effective approximation algorithm for multi-objective jobshop scheduling, multi-objective evolutionary algorithms (MOEAs) have received extensive attention. However, maintaining a balance between the diversity and convergence of non-dominated solutions while ensuring overall convergence is an open problem in the context of solving Multi-objective Fuzzy Flexible Jobshop Scheduling Problems (MFFJSPs)
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Decoupling Constraint: Task Clone-Based Multi-Tasking Optimization for Constrained Multi-Objective Optimization IEEE T. Evolut. Comput. (IF 11.7) Pub Date : 2024-01-26 Genghui Li, Zhenkun Wang, Weifeng Gao, Ling Wang
The coupling of multiple constraints can pose difficulties in solving constrained multi-objective optimization problems (CMOPs). Existing constrained multi-objective evolutionary algorithms (CMOEAs) often overlook this issue by considering all constraints together. This article proposes MTOTC, a novel multi-tasking optimization algorithm that addresses this challenge through a task clone technique
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On Cooperative Coevolution and Global Crossover IEEE T. Evolut. Comput. (IF 11.7) Pub Date : 2024-01-18 Larry Bull, Haixia Liu
Cooperative coevolutionary algorithms (CCEAs) divide a given problem in to a number of subproblems and use an evolutionary algorithm to solve each subproblem. This letter is concerned with the scenario under which a single fitness measure exists. By removing the typically used subproblem partnering mechanism, it is suggested that such CCEAs can be viewed as making use of a generalized version of the
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Knowledge Structure Preserving-Based Evolutionary Many-Task Optimization IEEE T. Evolut. Comput. (IF 11.7) Pub Date : 2024-01-18 Yi Jiang, Zhi-Hui Zhan, Kay Chen Tan, Sam Kwong, Jun Zhang