On Nonlinear Fitness Functions for Ranking-Based Selection
Vinicius L. S. Silva, André Rodrigues da Cruz, Eduardo Gontijo Carrano, Frederico Gadelha Guimarães, Ricardo H. C. Takahashi · 2006
This paper studies the issue of defining the fitness function for ranking-based selection. Two families of parametric nonlinear functions are considered, for reaching different selection pressures, controlled by the function parameter. Both the static versions and some dynamic varying versions of such functions are considered. The usual linear fitness function is shown to be systematically outperformed by several instances of nonlinear fitness. After a multiobjective analysis, it seems to be possible to recommend the usage of a specific static nonlinear fitness function.