IEEE Transactions on Evolutionary Computation最新文献_第9页

nLKH-ACS: A Niching Lin-Kernighan-Helsgaun Based Ant Colony System for Multi-Solution Traveling Salesman Problems nLKH-ACS：基于小生境的多解旅行商问题的蚁群系统

IF 14.3 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2024-11-28 DOI: 10.1109/tevc.2024.3507777

Ting Huang, Zhen-Quan Zhang, Yue-Jiao Gong, Jing Liu

引用次数: 0

Brain-Inspired Multi-Scale Evolutionary Neural Architecture Search for Deep Spiking Neural Networks 脑启发的多尺度进化神经结构搜索深度尖峰神经网络

IF 14.3 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2024-11-27 DOI: 10.1109/tevc.2024.3507812

Wenxuan Pan, Feifei Zhao, Guobin Shen, Bing Han, Yi Zeng

引用次数: 0

Evolutionary Computation in the Era of Large Language Model: Survey and Roadmap 大语言模型时代的进化计算：综述与路线图

IF 11.7 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2024-11-27 DOI: 10.1109/TEVC.2024.3506731

Xingyu Wu;Sheng-Hao Wu;Jibin Wu;Liang Feng;Kay Chen Tan

{"title":"Evolutionary Computation in the Era of Large Language Model: Survey and Roadmap","authors":"Xingyu Wu;Sheng-Hao Wu;Jibin Wu;Liang Feng;Kay Chen Tan","doi":"10.1109/TEVC.2024.3506731","DOIUrl":"10.1109/TEVC.2024.3506731","url":null,"abstract":"Large language models (LLMs) have not only revolutionized natural language processing but also extended their prowess to various domains, marking a significant stride toward artificial general intelligence. The interplay between LLMs and evolutionary algorithms (EAs), despite differing in objectives and methodologies, share a common pursuit of applicability in complex problems. Meanwhile, EA can provide an optimization framework for LLM’s further enhancement under closed box settings, empowering LLM with flexible global search capacities. On the other hand, the abundant domain knowledge inherent in LLMs could enable EA to conduct more intelligent searches. Furthermore, the text processing and generative capabilities of LLMs would aid in deploying EAs across a wide range of tasks. Based on these complementary advantages, this article provides a thorough review and a forward-looking roadmap, categorizing the reciprocal inspiration into two main avenues: 1) LLM-enhanced EA and 2) EA-enhanced LLM. Some integrated synergy methods are further introduced to exemplify the complementarity between LLMs and EAs in diverse scenarios, including code generation, software engineering, neural architecture search, and various generation tasks. As the first comprehensive review focused on the EA research in the era of LLMs, this article provides a foundational stepping stone for understanding the collaborative potential of LLMs and EAs. The identified challenges and future directions offer guidance for researchers and practitioners to unlock the full potential of this innovative collaboration in propelling advancements in optimization and artificial intelligence. We have created a GitHub repository to index the relevant papers: <uri>https://github.com/wuxingyu-ai/LLM4EC</uri>.","PeriodicalId":13206,"journal":{"name":"IEEE Transactions on Evolutionary Computation","volume":"29 2","pages":"534-554"},"PeriodicalIF":11.7,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142752927","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 Sparsity Knowledge Transfer-Based Evolutionary Algorithm for Large-Scale Multitasking Multi-Objective Optimization 用于大规模多任务多目标优化的基于稀疏性知识转移的进化算法

IF 14.3 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2024-11-15 DOI: 10.1109/tevc.2024.3498952

Chengming Wu, Ye Tian, Limiao Zhang, Xiaoshu Xiang, Xingyi Zhang

引用次数: 0

Transfer-Based Customized Bus Network Design With Holding Control and Heterogeneous Fleet 基于换乘的定制化公交网络设计，包含保持控制和异构车队

IF 14.3 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2024-11-14 DOI: 10.1109/tevc.2024.3498315

Yuwei Zhao, Ziyan Feng, Xiang Li

引用次数: 0

Multiform Differential Evolution With Elite-Guided Knowledge Transfer for Coal Mine Integrated Energy Systems Constrained Dispatch 煤矿综合能源系统受限调度的多形式差分进化与精英引导的知识转移

IF 14.3 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2024-11-13 DOI: 10.1109/tevc.2024.3496852

Canyun Dai, Xiaoyan Sun, Hejuan Hu, Wei Song, Yong Zhang, Dunwei Gong

引用次数: 0

LLaMEA: A Large Language Model Evolutionary Algorithm for Automatically Generating Metaheuristics LLaMEA：自动生成元搜索的大型语言模型进化算法

IF 11.7 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2024-11-13 DOI: 10.1109/TEVC.2024.3497793

Niki van Stein;Thomas Bäck

{"title":"LLaMEA: A Large Language Model Evolutionary Algorithm for Automatically Generating Metaheuristics","authors":"Niki van Stein;Thomas Bäck","doi":"10.1109/TEVC.2024.3497793","DOIUrl":"10.1109/TEVC.2024.3497793","url":null,"abstract":"Large language models (LLMs), such as GPT-4 have demonstrated their ability to understand natural language and generate complex code snippets. This article introduces a novel LLM evolutionary algorithm (LLaMEA) framework, leveraging GPT models for the automated generation and refinement of algorithms. Given a set of criteria and a task definition (the search space), LLaMEA iteratively generates, mutates, and selects algorithms based on performance metrics and feedback from runtime evaluations. This framework offers a unique approach to generating optimized algorithms without requiring extensive prior expertise. We show how this framework can be used to generate novel closed box metaheuristic optimization algorithms for box-constrained, continuous optimization problems automatically. LLaMEA generates multiple algorithms that outperform state-of-the-art optimization algorithms (covariance matrix adaptation evolution strategy and differential evolution) on the 5-D closed box optimization benchmark (BBOB). The algorithms also show competitive performance on the 10- and 20-D instances of the test functions, although they have not seen such instances during the automated generation process. The results demonstrate the feasibility of the framework and identify future directions for automated generation and optimization of algorithms via LLMs.","PeriodicalId":13206,"journal":{"name":"IEEE Transactions on Evolutionary Computation","volume":"29 2","pages":"331-345"},"PeriodicalIF":11.7,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10752628","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142610615","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

A Survey of Multi-objective Evolutionary Algorithm Based on Decomposition: Past and Future 基于分解的多目标进化算法概览：过去与未来

IF 14.3 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2024-11-12 DOI: 10.1109/tevc.2024.3496507

Ke Li

引用次数: 0

From Direct to Directional Variable Dependencies—Nonsymmetrical Dependencies Discovery in Real-World and Theoretical Problems 从直接变量依赖关系到定向变量依赖关系 - 在现实世界和理论问题中发现非对称依赖关系

IF 11.7 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2024-11-11 DOI: 10.1109/TEVC.2024.3496193

Michal Witold Przewozniczek;Bartosz Frej;Marcin Michal Komarnicki

{"title":"From Direct to Directional Variable Dependencies—Nonsymmetrical Dependencies Discovery in Real-World and Theoretical Problems","authors":"Michal Witold Przewozniczek;Bartosz Frej;Marcin Michal Komarnicki","doi":"10.1109/TEVC.2024.3496193","DOIUrl":"10.1109/TEVC.2024.3496193","url":null,"abstract":"The knowledge about variable interactions is frequently employed in state-of-the-art research concerning genetic algorithms (GAs). Whether these interactions are known a priori (gray-box optimization) or are discovered by the optimizer (black-box optimization), they are used for many purposes, including proposing more effective mixing operators. Frequently, the quality of the problem structure decomposition is decisive to the optimizers’ effectiveness. However, in gray- and black-box optimization, the dependency between the variables is assumed to be symmetric. This work identifies and defines the nonsymmetrical (directional) variable dependencies. We show that these dependencies may exist (together with symmetrical) in the considered real-world problem, in which we must optimize subsequent variable groups (one after the other) in the appropriate optimization order that is not known by the optimizer. To improve GA’s effectiveness in solving the problem of such features, we propose a new linkage learning (LL) technique that can discover symmetrical and nonsymmetrical dependencies (in binary and nonbinary discrete domains) and distinguish them from each other. We show that telling these two types of dependencies from each other may significantly increase the optimizer’s effectiveness in solving real-world and theoretical problems with nonsymmetrical dependencies. Finally, we show that using the proposed LL technique does not deteriorate the effectiveness of the state-of-the-art optimizer in solving typical benchmarks containing only symmetrical dependencies.","PeriodicalId":13206,"journal":{"name":"IEEE Transactions on Evolutionary Computation","volume":"29 2","pages":"490-504"},"PeriodicalIF":11.7,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10750302","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142599215","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 Multitasking With Adaptive Knowledge Transfer for Expensive Multiobjective Optimization 采用自适应知识转移的进化多任务技术实现昂贵的多目标优化

IF 14.3 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2024-11-07 DOI: 10.1109/tevc.2024.3494039

Xunfeng Wu, Songbai Liu, Qiuzhen Lin, Kay Chen Tan, Victor C. M. Leung

引用次数: 0