A multiple-population genetic algorithm for branch coverage test data generation

2010 ◽  
Vol 19 (3) ◽  
pp. 489-513 ◽  
Author(s):  
Mohammad Alshraideh ◽  
Basel A. Mahafzah ◽  
Saleh Al-Sharaeh
Keyword(s):  
Genetic Algorithm ◽  
Test Data ◽  
Population Genetic ◽  
Test Data Generation ◽  
Data Generation ◽  
Branch Coverage ◽  
Multiple Population ◽  
Population Genetic Algorithm

scholarly journals Study on MPGA-BP of Gravity Dam Deformation Prediction

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Xiaoyu Wang ◽  
Kan Yang ◽  
Changsong Shen
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Population Genetic ◽  
Prediction Accuracy ◽  
Gravity Dam ◽  
Hydraulic Structures ◽  
Deformation Prediction ◽  
Multiple Population ◽  
Population Genetic Algorithm

Displacement is an important physical quantity of hydraulic structures deformation monitoring, and its prediction accuracy is the premise of ensuring the safe operation. Most existing metaheuristic methods have three problems: (1) falling into local minimum easily, (2) slowing convergence, and (3) the initial value’s sensitivity. Resolving these three problems and improving the prediction accuracy necessitate the application of genetic algorithm-based backpropagation (GA-BP) neural network and multiple population genetic algorithm (MPGA). A hybrid multiple population genetic algorithm backpropagation (MPGA-BP) neural network algorithm is put forward to optimize deformation prediction from periodic monitoring surveys of hydraulic structures. This hybrid model is employed for analyzing the displacement of a gravity dam in China. The results show the proposed model is superior to an ordinary BP neural network and statistical regression model in the aspect of global search, convergence speed, and prediction accuracy.

Sign in / Sign up

Export Citation Format

Share Document