Self-Adaptive Genetic Algorithms with Simulated Binary Crossover

Authors:
Kalyanmoy Deb;Hans-georg Beyer
Affiliations:
Kanpur Genetic Algorithms Laboratory, Department of Mechanical Engineering, Indian Institute of Technology Kanpur, PIN 208 016, India;Systems Analysis Group, Department of Computer Science XI, University of Dortmund, D-44221 Dortmund, Germany
Venue:
Evolutionary Computation
Year:
2001

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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.