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Using the genetic algorithm to build optimal neural networks for fault-prone module detection
被引:12
作者
:
Hochman, R
论文数:
0
引用数:
0
h-index:
0
机构:
FLORIDA ATLANTIC UNIV,DEPT COMP SCI & ENGN,BOCA RATON,FL 33431
FLORIDA ATLANTIC UNIV,DEPT COMP SCI & ENGN,BOCA RATON,FL 33431
Hochman, R
[
1
]
Khoshgoftaar, TM
论文数:
0
引用数:
0
h-index:
0
机构:
FLORIDA ATLANTIC UNIV,DEPT COMP SCI & ENGN,BOCA RATON,FL 33431
FLORIDA ATLANTIC UNIV,DEPT COMP SCI & ENGN,BOCA RATON,FL 33431
Khoshgoftaar, TM
[
1
]
Allen, EB
论文数:
0
引用数:
0
h-index:
0
机构:
FLORIDA ATLANTIC UNIV,DEPT COMP SCI & ENGN,BOCA RATON,FL 33431
FLORIDA ATLANTIC UNIV,DEPT COMP SCI & ENGN,BOCA RATON,FL 33431
Allen, EB
[
1
]
Hudepohl, JP
论文数:
0
引用数:
0
h-index:
0
机构:
FLORIDA ATLANTIC UNIV,DEPT COMP SCI & ENGN,BOCA RATON,FL 33431
FLORIDA ATLANTIC UNIV,DEPT COMP SCI & ENGN,BOCA RATON,FL 33431
Hudepohl, JP
[
1
]
机构
:
[1]
FLORIDA ATLANTIC UNIV,DEPT COMP SCI & ENGN,BOCA RATON,FL 33431
来源
:
SEVENTH INTERNATIONAL SYMPOSIUM ON SOFTWARE RELIABILITY ENGINEERING, PROCEEDINGS
|
1996年
关键词
:
backpropagation;
classification problem;
fault-prone module;
fitness function;
genetic algorithm;
neural network;
simulated evolution;
software engineering problem;
software metrics;
software quality;
D O I
:
10.1109/ISSRE.1996.558759
中图分类号
:
TP31 [计算机软件];
学科分类号
:
081202 ;
0835 ;
摘要
:
引用
收藏
页码:152 / 162
页数:11
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