Self-adaptation in real-parameter genetic algorithms with simulated binary crossover

Abstract

In the context of function optimization, selfadaptation features of evolutionary search algorithms have been explored only with evolution strategy (ES) and evolutionary programming (EP). In this paper, we demonstrate the self-adaptive feature of real-parameter genetic algorithms (GAs) using the simulated binary crossover (SBX) operator. The connection between the working of selfadaptive ESs and real-parameter GAs with SBX operator is also discussed. The self-adaptive behavior of real-parameter GAs is demonstrated on a number of test problems commonly-used in the ES literature. The remarkable similarity in the working of real-parameter GAs and self-adaptive ESs shown in this study suggests the need of emphasizing further studies on self-adaptive GAs.