A comparative study on the effects of adding perturbations to phenotypic parameters in genetic algorithms with a robust solution searching scheme

We have proposed a scheme that extends the application of GAs to domains that require identification of robust solutions. We called this technique GAs/RS³: GAs with a robust solution searching scheme. In the GAs/RS³, a perturbation is added to the phenotypic feature once for each evaluating functional value of an individual, thereby reducing the chance of selecting sharp peaks. We refer to this method as a single-evaluation mode (SEM). We introduce a natural variant of this method, a multi-evaluation mode (MEM), where perturbations are given more than one time for each evaluating functional value of an individual, and we offer comparative studies on the convergence property. The results showed that for the GAs/RS³ with SEM the population converges to robust solutions faster than with the MEM, and as the number of evaluations increases, the convergence speed decreases. We may conclude that the GAs/RS³ with the SEM is more efficient than with the MEM. We also introduce a variation of MEM, i.e., multi-evaluation mode with the worst value (MEM-W) and provide a mathematical analysis