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:

    scopusscopus

    Tipo de documento:

    Conference Object

    Estado:

    Acceso restringido

    Áreas de conocimiento:

    • Algoritmo
    • Algoritmo
    • Algoritmo

    Áreas temáticas:

    • Ciencias de la computación