.png){kind=icon}
返回
Evolutionary Multitasking via Explicit Autoencoding
.png){kind=icon}
PRE
AI
DOI：10.1109/TCYB.2018.2845361.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Evolutionary multitasking (EMT) is an emerging research topic in the field of evolutionary computation. In contrast to the traditional single-task evolutionary search, EMT conducts evolutionary search on multiple tasks simultaneously. It aims to improve convergence characteristics across multiple optimization problems at once by seamlessly transferring knowledge among them. Due to the efficacy of EMT, it has attracted lots of research attentions and several EMT algorithms have been proposed in the literature. However, existing EMT algorithms are usually based on a common mode of knowledge transfer in the form of implicit genetic transfer through chromosomal crossover. This mode cannot make use of multiple biases embedded in different evolutionary search operators, which could give better search performance when properly harnessed. Keeping this in mind, this paper proposes an EMT algorithm with explicit genetic transfer across tasks, namely EMT via autoencoding, which allows the incorporation of multiple search mechanisms with different biases in the EMT paradigm. To confirm the efficacy of the proposed EMT algorithm with explicit autoencoding, comprehensive empirical studies have been conducted on both the single-and multi-objective multitask optimization problems.
Keyword:
Autoencoder
evolutionary optimization
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:
10.5
论文数:
1.1W
被引数:
5.0W
机构
引用论文
.png){kind=icon}
分享收藏Evolutionary Dynamic Multiobjective Optimization: Benchmarks and Algorithm Comparisons进化动态多目标优化: 基准和算法比较
PREAI
分享收藏
Transfer Boosting With Synthetic Instances for Class Imbalanced Object Recognition使用合成实例进行类不平衡对象识别的转移增强
PREAI
分享收藏
分享收藏分享收藏
查看更多内容