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

A Joint-Encoding Evolutionary Algorithm for Multimodal Multiobjective Feature Selection in Classification 多模态多目标分类特征选择的联合编码进化算法

IF 14.3 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2025-01-16 DOI: 10.1109/tevc.2025.3529977

Jing Liang, Junting Yang, Caitong Yue, Ying Bi, Kunjie Yu, Boyang Qu, Yuyang Zhang, Mengmeng Li

引用次数: 0

Automatic Fuzzy Architecture Design for Defect Detection via Classifier-Assisted Multiobjective Optimization Approach 基于分类器辅助多目标优化的缺陷检测自动模糊体系结构设计

IF 14.3 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2025-01-16 DOI: 10.1109/tevc.2025.3530416

Nan Li, Bing Xue, Lianbo Ma, Mengjie Zhang

引用次数: 0

Zeroth-Order Actor–Critic: An Evolutionary Framework for Sequential Decision Problems 零阶行为批判者：序列决策问题的进化框架

IF 11.7 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2025-01-14 DOI: 10.1109/TEVC.2025.3529503

Yuheng Lei;Yao Lyu;Guojian Zhan;Tao Zhang;Jiangtao Li;Jianyu Chen;Shengbo Eben Li;Sifa Zheng

{"title":"Zeroth-Order Actor–Critic: An Evolutionary Framework for Sequential Decision Problems","authors":"Yuheng Lei;Yao Lyu;Guojian Zhan;Tao Zhang;Jiangtao Li;Jianyu Chen;Shengbo Eben Li;Sifa Zheng","doi":"10.1109/TEVC.2025.3529503","DOIUrl":"10.1109/TEVC.2025.3529503","url":null,"abstract":"Evolutionary algorithms (EAs) have shown promise in solving sequential decision problems (SDPs) by simplifying them to static optimization problems and searching for the optimal policy parameters in a zeroth-order way. Despite their versatility, EAs often suffer from high sample complexity due to neglecting underlying temporal structures. In contrast, reinforcement learning (RL) methods typically formulate SDPs as Markov decision process (MDP). Although more sample efficient than EAs, RL methods are restricted to differentiable policies and prone to getting stuck in local optima. To address these issues, we propose a novel evolutionary framework zeroth-order actor-critic (ZOAC). We propose to use stepwise exploration in parameter space and theoretically derive the zeroth-order policy gradient. We further utilize the actor-critic architecture to effectively leverage the Markov property of SDPs and reduce the variance of gradient estimators. In each iteration, ZOAC collects trajectories with parameter space exploration, and alternates between first-order policy evaluation (PEV) and zeroth-order policy improvement (PIM). We evaluate the effectiveness of ZOAC on a challenging multilane driving task optimizing the parameters in a rule-based, nondifferentiable driving policy that consists of three submodules: 1) behavior selection; 2) path planning; and 3) trajectory tracking. We also compare it with gradient-based RL methods on three Gymnasium tasks, optimizing neural network policies with thousands of parameters. Experimental results demonstrate the strong capability of ZOAC in solving SDPs. ZOAC significantly outperforms EAs that treat the problem as static optimization and matches the performance of gradient-based RL methods even without first-order information, in terms of total average return across tasks.","PeriodicalId":13206,"journal":{"name":"IEEE Transactions on Evolutionary Computation","volume":"29 2","pages":"555-569"},"PeriodicalIF":11.7,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142981488","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 Multi-Objective Spiking Neural Architecture Search for Image Classification 用于图像分类的进化多目标尖峰神经架构搜索

IF 14.3 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2025-01-13 DOI: 10.1109/tevc.2025.3528471

Xiaotian Song, Zeqiong Lv, Jiaohao Fan, Xiong Deng, Jiancheng Lv, Jiyuan Liu, Yanan Sun

引用次数: 0

Historical Information-assisted Dynamic Response Integration and Adaptive Niche Methods for Dynamic Multimodal Optimization 历史信息辅助的动态响应集成与自适应小生境动态多模态优化方法

IF 14.3 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2025-01-09 DOI: 10.1109/tevc.2025.3527478

Kunjie Yu, Xuyang Zhang, Dezheng Zhang, Jing Liang, Yumeng Li, Heshan Wang, Ke Chen, Caitong Yue

引用次数: 0

Multiform Genetic Programming Framework for Symbolic Regression Problems 符号回归问题的多形式遗传规划框架

IF 11.7 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2025-01-09 DOI: 10.1109/TEVC.2025.3527875

Jinghui Zhong;Junlan Dong;Wei-Li Liu;Liang Feng;Jun Zhang

{"title":"Multiform Genetic Programming Framework for Symbolic Regression Problems","authors":"Jinghui Zhong;Junlan Dong;Wei-Li Liu;Liang Feng;Jun Zhang","doi":"10.1109/TEVC.2025.3527875","DOIUrl":"10.1109/TEVC.2025.3527875","url":null,"abstract":"genetic programming (GP) is a widely recognized and powerful approach for symbolic regression (SR) problems. However, existing GP methods rely on a single form to solve the problem, which limits their search diversity and increases the likelihood of getting stuck in local optima, especially in complex scenarios. In this article, we propose a general multiform GP (MFGP) framework to improve the performance of GP on complicated SR problems. As far as we know, this articel is the first attempt to integrate the multiform optimization paradigm with GP to accelerate the search performance. The key idea of the proposed framework is to construct multiple forms to solve the same problem cooperatively at the same time. During the evolution process, knowledge gained from different forms is shared among the solvers to improve the search diversity and efficiency. A knowledge transfer mechanism is specifically designed to facilitate knowledge transfer among GP solvers with different modeling forms. In addition, an adaptive resource control mechanism is designed to reallocate computing resources according to the problem solving efficiency of different solvers to further improve search efficiency. To demonstrate the effectiveness of the proposed framework, a multiform gene expression programming algorithm is designed and tested on 20 problems, including physical datasets, synthetic datasets, and real-world datasets. The experimental results have demonstrated the effectiveness of the proposed framework.","PeriodicalId":13206,"journal":{"name":"IEEE Transactions on Evolutionary Computation","volume":"29 2","pages":"429-443"},"PeriodicalIF":11.7,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142940442","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

Multi-Agent Evolution Strategy With Cooperative and Cumulative Step Adaptation for Black-Box Distributed Optimization 黑盒分布优化的协同与累积步适应多智能体进化策略

IF 14.3 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2025-01-06 DOI: 10.1109/tevc.2025.3525713

Tai-You Chen, Wei-Neng Chen, Jin-Kao Hao, Yang Wang, Jun Zhang

引用次数: 0

Multi-Task Multi-Scale Feature Selection for Point Cloud Registration 点云配准的多任务多尺度特征选择

IF 14.3 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2025-01-06 DOI: 10.1109/tevc.2025.3526779

Yue Wu, Chuang Luo, Maoguo Gong, Hangqi Ding, Jinlong Sheng, Qiguang Miao, Hao Li, Wenping Ma, Hao He

引用次数: 0

Problem Decomposition Strategies and Credit Distribution Mechanisms in Modular Genetic Programming for Supervised Learning 基于监督学习的模块化遗传规划问题分解策略与信用分配机制

IF 14.3 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2025-01-06 DOI: 10.1109/tevc.2025.3526581

Lino Rodriguez-Coayahuitl, Ansel Y. Rodríguez-González, Daniel Fajardo-Delgado, Maria Guadalupe Sánchez Cervantes

引用次数: 0

A Subspace Sparsity Driven Knowledge Transfer Strategy for Dynamic Constrained Multiobjective Optimization 基于子空间稀疏性的动态约束多目标优化知识转移策略

IF 14.3 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2025-01-03 DOI: 10.1109/tevc.2025.3525635

Guoyu Chen, Yinan Guo, Changhe Li, Feng Wang, Dunwei Gong, Liang Yuan

引用次数: 0