.png){kind=icon}
返回
Multifactorial Genetic Programming for Symbolic Regression Problems
.png){kind=icon}
PRE
AI
DOI：10.1109/TSMC.2018.2853719.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Genetic programming (GP) is a powerful evolutionary algorithm that has been widely used for solving many real-world optimization problems. However, traditional GP can only solve a single task in one independent run, which is inefficient in cases where multiple tasks need to be solved at the same time. Recently, multifactorial optimization (MFO) has been proposed as a new evolutionary paradigm toward evolutionary multitasking. It intends to conduct evolutionary search on multiple tasks in one independent run. To enable multitasking GP, in this paper, we propose a novel multifactorial GP (MFGP) algorithm. To the best of our knowledge, this is the first attempt in the literature to conduct multitasking GP using a single population. The proposed MFGP consists of a novel scalable chromosome encoding scheme which is capable of representing multiple solutions simultaneously, and new evolutionary mechanisms for MFO based on self-learning gene expression programming. Further, comprehensive experimental studies are conducted on multitask scenarios consisting of commonly used GP benchmark problems and real world applications. The obtained empirical results confirmed the efficacy of the proposed MFGP.
Keyword:
Task analysis
Sociology
Statistics
Multitasking
Optimization
Search problems
Programming
Genetic programming (GP)
multifactorial evolutionary algorithm (MFEA)
multifactorial optimization (MFO)
multitask learning (MTL)
symbolic regression problem (SRP)
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}
分享收藏分享收藏分享收藏Deep Additive Least Squares Support Vector Machines for Classification With Model Transfer基于模型转移的深度加法最小二乘支持向量机分类
OAAI
OAAI分享收藏
分享收藏
查看更多内容