Orthogonal Genetic Algorithms with Application to Multimedia Multicast Routing

2004 ◽  
pp. 147-174
Author(s):  
Yiu-Wing Leung
Keyword(s):  
Genetic Algorithms ◽  
Multicast Routing

Group multicast routing problem: A genetic algorithms based approach

2007 ◽  
Vol 51 (14) ◽  
pp. 3989-4004 ◽  
Author(s):  
L. Sanna Randaccio ◽  
L. Atzori
Keyword(s):  
Genetic Algorithms ◽  
Multicast Routing ◽  
Routing Problem

scholarly journals Analyzing the Performance of the Multiple-Searching Genetic Algorithm to Generate Test Cases

2020 ◽  
Vol 10 (20) ◽  
pp. 7264
Author(s):  
Wanida Khamprapai ◽  
Cheng-Fa Tsai ◽  
Paohsi Wang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Execution Time ◽  
Multicast Routing ◽  
Test Case ◽  
Test Cases ◽  
Mutation Operator ◽  
Optimal Solutions ◽  
Crossover Operator ◽  
Network Systems

Software testing using traditional genetic algorithms (GAs) minimizes the required number of test cases and reduces the execution time. Currently, GAs are adapted to enhance performance when finding optimal solutions. The multiple-searching genetic algorithm (MSGA) has improved upon current GAs and is used to find the optimal multicast routing in network systems. This paper presents an analysis of the optimization of test case generations using the MSGA by defining suitable values of MSGA parameters, including population size, crossover operator, and mutation operator. Moreover, in this study, we compare the performance of the MSGA with a traditional GA and hybrid GA (HGA). The experimental results demonstrate that MSGA reaches the maximum executed branch statements in the lowest execution time and the smallest number of test cases compared to the GA and HGA.

A Genetic Algorithms Based Approach for Group Multicast Routing

2006 ◽  
Vol 1 (4) ◽  
Author(s):  
Luca Sanna Randaccio ◽  
Luigi Atzori
Keyword(s):  
Genetic Algorithms ◽  
Multicast Routing
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