title: Self-adaptive genetic algorithms with simulated binary crossover.
abstract: Self-adaptation is an essential feature of natural evolution. However, in the context of function optimization, self-adaptation features of evolutionary 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 simulated binary crossover (SBX) operator and without any mutation operator. The connection between the working of self-adaptive ESs and real-parameter GAs with the SBX operator is also discussed. Thereafter, 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 principle of real-parameter GAs and self-adaptive ESs shown in this study suggests the need for emphasizing further studies on self-adaptive GAs.
authors.affiliation: Kanpur Genetic Algorithms Laboratory, Department of Mechanical Engineering, Indian Institute of Technology Kanpur, PIN 208 016, India. deb@iitk.ac.in
authors.forename: K
authors.initials: K
authors.lastname: Deb
authors.forename: H G
authors.initials: HG
authors.lastname: Beyer
mesh_terms: D000465:Algorithms; D015982:Bias; D005075:Biological Evolution; D003198:Computer Simulation; D005828:Genetics, Population; D008957:Models, Genetic; D015233:Models, Statistical; D009154:Mutation