Parallel varying mutation in deterministic and self-adaptive GAs
Abstract:
In this work we study varying mutations applied either serial or parallel to crossover and discuss its effect on the performance of deterministic and self-adaptive varying mutation GAs. After comparative experiments, we found that varying mutation parallel to crossover can be a more effective framework in both deterministic and self-adaptive GAs to achieve faster convergence velocity and higher convergence reliability. Best performance is achieved by a parallel varying mutation self-adaptive GA.
Año de publicación:
2002
Keywords:
Fuente:
scopus
Tipo de documento:
Conference Object
Estado:
Acceso restringido
Áreas de conocimiento:
- Algoritmo
- Algoritmo
- Algoritmo
Áreas temáticas:
- Ciencias de la computación