scholarly journals Optimization of test/diagnosis/rework location(s) and characteristics in electronic systems assembly using real-coded genetic algorithms

2004 ◽  
Author(s):  
Zhen Shi ◽  
P. Sandborn
Keyword(s):  
Genetic Algorithms ◽  
Electronic Systems ◽  
Real Coded Genetic Algorithms ◽  
Systems Assembly

Robust confidence intervals applied to crossover operator for real-coded genetic algorithms

2007 ◽  
Vol 12 (8) ◽  
pp. 809-833 ◽  
Author(s):  
Domingo Ortiz-Boyer ◽  
César Hervás-Martínez ◽  
Nicolás García-Pedrajas
Keyword(s):  
Genetic Algorithms ◽  
Confidence Intervals ◽  
Crossover Operator ◽  
Real Coded Genetic Algorithms

scholarly journals Analysis of The Behavior of MGG and JGG As A Selection Model for Real-coded Genetic Algorithms

2010 ◽  
Vol 25 ◽  
pp. 281-289 ◽  
Author(s):  
Youhei Akimoto ◽  
Yuichi Nagata ◽  
Jun Sakuma ◽  
Isao Ono ◽  
Shigenobu Kobayashi
Keyword(s):  
Genetic Algorithms ◽  
Selection Model ◽  
Real Coded Genetic Algorithms

A Hybrid Genetic-Direct Search Algorithm for the Shape Optimization of Solid C-Frame Cross-Sections

2000 ◽  
Author(s):  
Ashraf O. Nassef ◽  
Hesham A. Hegazi ◽  
Sayed M. Metwalli
Keyword(s):  
Genetic Algorithms ◽  
Shape Optimization ◽  
Cross Section ◽  
Random Search ◽  
Direct Search ◽  
Search Method ◽  
Optimization Techniques ◽  
Hybrid Optimization ◽  
The Cross ◽  
Real Coded Genetic Algorithms

Abstract The hybridization of different optimization methods have been used to find the optimum solution of design problems. While random search techniques, such as genetic algorithms and simulated annealing, have a high probability of achieving global optimality, they usually arrive at a near optimal solution due to their random nature. On the other hand direct search methods are efficient optimization techniques but linger in local minima if the objective function is multi-modal. This paper presents the optimization of C-frame cross-section using a hybrid optimization algorithm. Real coded genetic algorithms are used as a random search method, while Nelder-Mead is used as a direct search method, where the result of the genetic algorithm search is used as the starting point of direct search. Traditionally, the cross-section of C-frame belonged to a set of primitive shapes, which included I, T, trapezoidal, circular and rectangular sections. The cross-sectional shape is represented by a non-uniform rational B-Splines (NURBS) in order to give it a kind of shape flexibility. The results showed that the use of Nelder-Mead with Real coded Genetic Algorithms has been very significant in improving the optimum shape of a solid C-frame cross-section subjected to a combined tension and bending stresses. The hybrid optimization method could be extended to more complex shape optimization problems.

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