.png){kind=icon}
返回
Best practices in measuring algorithm performance for dynamic optimization problems
.png){kind=icon}
PRE
AI
DOI：10.1007/s00500-013-0989-7.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Dynamic optimization problems (DOPs) have attracted considerable attention due to the wide range of problems they can be applied to. Lots of efforts have been expended in modeling dynamic situations, proposing algorithms, and analyzing the results (too often in a visual way). Numeric performance measurements and their statistical validation have been however barely used in the literature. Most of works in DOPs report only the best-of-generation fitness, due to its simplicity of computation. Although this measure indicates the best algorithm in terms of fitness, it does not provide any details about the actual strength and weakness of each algorithm. In this article, we conduct a comparative study among algorithms of different search modes via several performance measures to demonstrate their relative advantages. We discuss the role of using different performance measures in drawing balanced conclusions about algorithms for DOPs.
Keyword:
Dynamic optimization problems
Evolutionary algorithms
Genetic algorithms
Performance measure
期刊
IF:
2.5
论文数:
1.0W
被引数:
2.1W
机构
引用论文
.png){kind=icon}
分享收藏
Defense Strategies of Rice in Response to the Attack of the Herbivorous Insect, Chilo suppressalis
OAAI
OAAI分享收藏分享收藏
查看更多内容