.png){kind=icon}
返回
A Directed Genetic Algorithm for global optimization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.amc.2012.12.046.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Within the framework of real-coded genetic algorithms, this paper proposes a directed genetic algorithm (DGA) that introduces a directed crossover operator and a directed mutation operator. The operation schemes of these operators borrow from the reflection and the expansion search mode of the Nelder-Mead's simplex method. First, the Taguchi method is employed to study the influence analysis of the parameters in the DGA. The results show that the parameters in the DGA have strong robustness for solving the global optimal solution. Then, several strategies are proposed to enhance the solution accuracy capability of the DGA. All of the strategies are applied to a set of 30/100-dimensional benchmark functions to prove their superiority over several genetic algorithms. Finally, a cantilevered beam design problem with constrained conditions is used as a practical structural optimization example for demonstrating the very good performance of the proposed method. (C) 2012 Elsevier Inc. All rights reserved.
Keyword:
Directed genetic algorithm
Nelder-Mead's simplex algorithm
Global 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:
3.4
论文数:
2.3W
被引数:
3.3W
机构
引用论文
分享收藏Cyclin A-kinase regulation of E2F-1 DNA binding function underlies suppression of an S phase checkpoint.png)
Cell
OAAI
CellIF0
OAAI分享收藏A comparison of optical and radar measurements of mesospheric winds and tides对中层大气风场和潮汐的光学测量与雷达测量的比较OAAI
OAAI分享收藏
分享收藏
查看更多内容