Self-adaptation is an essential feature of natural evolution. However, in t
he context of function optimization, self-adaptation features of evolutiona
ry search algorithms have been explored mainly with evolution strategy (ES)
and evolutionary programming (EP). In this paper, we demonstrate the self-
adaptive feature of real-parameter genetic algorithms (GAs) using a simulat
ed binary crossover (SBX) operator and without any mutation operator. The c
onnection between the working of self-adaptive ESs and real-parameter GAs w
ith the SBX operator is also discussed. Thereafter, the self-adaptive behav
ior of real-parameter GAs is demonstrated on a number of test problems comm
only used in the ES literature. The remarkable similarity in the working pr
inciple of real-parameter GAs and self-adaptive ESs shown in this study sug
gests the need for emphasizing further studies on self-adaptive GAs.