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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.