.png){kind=icon}
返回
A Two-Level Transfer Learning Algorithm for Evolutionary Multitasking
.png){kind=icon}
OA
AI
DOI：10.3389/fnins.2019.01408.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Different from conventional single-task optimization, the recently proposed multitasking optimization (MTO) simultaneously deals with multiple optimization tasks with different types of decision variables. MTO explores the underlying similarity and complementarity among the component tasks to improve the optimization process. The well-known multifactorial evolutionary algorithm (MFEA) has been successfully introduced to solve MTO problems based on transfer learning. However, it uses a simple and random inter-task transfer learning strategy, thereby resulting in slow convergence. To deal with this issue, this paper presents a two-level transfer learning (TLTL) algorithm, in which the upper-level implements inter-task transfer learning via chromosome crossover and elite individual learning, and the lower-level introduces intra-task transfer learning based on information transfer of decision variables for an across-dimension optimization. The proposed algorithm fully uses the correlation and similarity among the component tasks to improve the efficiency and effectiveness of MTO. Experimental studies demonstrate the proposed algorithm has outstanding ability of global search and fast convergence rate.
Keyword:
evolutionary multitasking
multifactorial optimization
transfer learning
memetic algorithm
knowledge transfer
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.2
论文数:
1.6W
被引数:
5.3W
机构
引用论文
.png){kind=icon}
分享收藏分享收藏
查看更多内容