Self-adaptation in real-parameter genetic algorithms with simulated binary crossover
Kalyanmoy Deb, Hans-Georg Beyer · 1999
In the context of function optimization, self-adaptation 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 realparameter 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. 1 Introduction Self-adaptation is a phenomenon which makes evolutionary search algorithms flexible and closer to natural evolution. Among the evolutionary methods, self-adaptation properties are explored with evolution strate...