.png){kind=icon}
返回
Hybrid genetic algorithm based on quantum computing for numerical optimization and parameter estimation
.png){kind=icon}
PRE
AI
DOI：10.1016/j.amc.2005.01.115.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Quantum computing is applied to genetic algorithm (GA) to develop a class of quantum-inspired genetic algorithm (QGA) characterized by certain principles of quantum mechanisms for numerical optimization. Furthermore, a framework of hybrid QGA, named RQGA, is proposed by reasonably combining the Q-bit search of quantum algorithm in micro-space and classic genetic search of real-coded GA (RGA) in macro-space to achieve better optimization performances. Simulation results based on typical functions demonstrate the effectiveness of the hybridization, especially the superiority of RQGA in terms of optimization quality, efficiency as well as the robustness on parameters and initial conditions. Moreover, simulation results about model parameter estimation also demonstrate the effectiveness and efficiency of the RQGA. (c) 2005 Elsevier Inc. All rights reserved.
Keyword:
quantum computing
genetic algorithm
hybrid algorithm
numerical optimization
parameter estimation
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:
3.4
论文数:
2.3W
被引数:
3.3W
机构
暂无机构信息
引用论文
Sources of mortality for a cynipid gall-wasp (Dryocosmus dubiosus (Hymenoptera: Cynipidae)): The importance of the Tannin/Fungus interaction.png)
Oecologia
OecologiaIF0
PREAI
分享收藏New eddy-current sensor setup for high-resolution lithium-ion cell dilation measurements用于高分辨率锂离子电池膨胀测量的新型涡流传感器设置
OAAI
IF0
OAAI分享收藏
没有更多内容
没有更多内容