.png){kind=icon}
返回
An Adaptive Archive-Based Evolutionary Framework for Many-Task Optimization
.png){kind=icon}
PRE
AI
DOI：10.1109/TETCI.2019.2916051.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Multi-task optimization is an emerging research topic in computational intelligence community. In this paper, we propose a novel evolutionary framework, many-task evolutionary algorithm (MaTEA), for many-task optimization. In the proposed MaTEA, a n adaptive selection mechanism is proposed to select suitable assisted task for a given task by considering the similarity between tasks and the accumulated rewards of knowledge transfer during the evolution. Besides, a knowledge transfer schema via crossover is adopted to exchange information among tasks to improve the search efficiency. In addition, to facilitate measuring similarity between tasks and transferring knowledge among tasks that arrive at different time instances, multiple archives are integrated with the proposed MaTEA. Experiments on both single-objective and multi-objective optimization problems have demonstrated that the proposed MaTEA can outperform the state-of-the-art multi-task evolutionary algorithms, in terms of search efficiency and solution accuracy. Besides, the proposed MaTEA is also capable of solving dynamic many-task optimization where tasks arrive at different time instances.
Keyword:
Evolutionary algorithm
multi-task optimization
many-task optimization
dynamic control
adaptive strategy
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
I
IF:
6.5
论文数:
1.4K
被引数:
4.5K
机构
引用论文
分享收藏Identification of major consensus QTLs for seed size and minor QTLs for pod traits in cultivated groundnut (Arachis hypogaea L.).png)
3 Biotech
OAAI
3 BiotechIF0
OAAI分享收藏
分享收藏Contribution of the hindgut to digestion of diets in growing pigs and adult sows: effect of diet composition后肠对生长猪和成年母猪日粮消化的贡献: 日粮组成的影响
PREAI
分享收藏分享收藏
查看更多内容