.png){kind=icon}
返回
Multiobjective Multitasking Optimization Based on Incremental Learning
.png){kind=icon}
PRE
AI
DOI：10.1109/TEVC.2019.2962747.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Multiobjective multitasking optimization (MTO) is an emerging research topic in the field of evolutionary computation. In contrast to multiobjective optimization, MTO solves multiple optimization tasks simultaneously. MTO aims to improve the overall performance of multiple tasks through knowledge transfer among tasks. Recently, MTO has attracted the attention of many researchers, and several algorithms have been proposed in the literature. However, one of the crucial issues, finding useful knowledge, has been rarely studied. Keeping this in mind, this article proposes an MTO algorithm based on incremental learning (EMTIL). Specifically, the transferred solutions (the form of knowledge) will be selected by incremental classifiers, which are capable of finding valuable solutions for knowledge transfer. The training data are generated by the knowledge transfer at each generation. Furthermore, the search space of the tasks will be explored by the proposed mapping (among tasks) approach, which helps these tasks to escape from their local Pareto Fronts. Empirical studies have been conducted on 15 MTO problems to assess the effectiveness of EMTIL. The experimental results demonstrate that EMTIL works more effectively for MTO compared to the existing algorithms.
Keyword:
Task analysis
Optimization
Knowledge transfer
Evolutionary computation
Heuristic algorithms
Problem-solving
Cloud computing
Evolutionary algorithms
incremental learning
knowledge transfer
multitask optimization
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:
12
论文数:
1.9K
被引数:
2.7W
机构
引用论文
Large-Scale Inoculum Production of Arbuscular Mycorrhizal Fungi on Root Organs and Inoculation Strategies.png)
Soil Biology
Soil BiologyIF0
PREAI
分享收藏Multiobjective evolutionary algorithms: A comparative case study and the Strength Pareto approach多目标进化算法: 比较案例研究和强度帕累托方法
PREAI
分享收藏
A Genetic Bankrupt Ratio Analysis Tool Using a Genetic Algorithm to Identify Influencing Financial Ratios使用遗传算法识别影响财务比率的遗传破产比率分析工具
PREAI
分享收藏
查看更多内容