A fitness guided mutation operator for improved performance of MOEAs

In this paper we present a new fitness guided version of the classical polynomial mutation operator. The experimental results show that the proposed fitness guided polynomial mutation (FGPLM) operator outperforms the classical polynomial mutation operator when applied in Non-dominated Sorting Genetic Algorithm II (NSGAII) in a number of performance measures that evaluate the proximity of the solutions to the Pareto front.