.png){kind=icon}
返回
A new multiobjective evolutionary algorithm
.png){kind=icon}
PRE
AI
DOI：10.1016/S0377-2217(01)00190-4.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The Pareto-based approaches have shown some success in designing multiobjective evolutionary algorithms (MEAs). Their methods of fitness assignment are mainly from the information of dominated and nondominated individuals. On the top of the hierarchy of MEAs, the strength Pareto evolutionary algorithm (SPEA) has been elaborately designed with this principle in mind. In this paper, we propose a (mu+lambda) multiobjective evolutionary algorithm ((mu+lambda) MEA), which discards the dominated individuals in each generation. The comparisons of the experimental results demonstrate that the (mu+lambda) MEA outperforms SPEA on five benchmark functions with less computational efforts. (C) 2002 Elsevier Science B.V. All rights reserved.
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:
6
论文数:
2.2W
被引数:
6.4W
机构
暂无机构信息
引用论文
Post‐stroke depression and functional recovery in a population‐based stroke register. The Finnstroke study基于人群的卒中登记中的卒中后抑郁和功能恢复。Finnstroke研究
.png){kind=icon}
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Printable cellulose-based electroconductive composites for sensing elements in paper electronics
OAAI
OAAI分享收藏Monodisperse single-crystal mesoporous magnetite nanoparticles induced by nanoscale gas bubbles
PREAI
分享收藏
查看更多内容