Work

Self-Adaptive Genetic Algorithms with Simulated Binary Crossover

Year: 2001

Type: article

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.

Source: Evolutionary Computation

DOI: 10.1162/106365601750190406

Authors: Kalyanmoy Deb, Hans-Georg Beyer

Institutions: Indian Institute of Technology Kanpur, TU Dortmund University

Language: English

Topic: Metaheuristic Optimization Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Alexander von Humboldt-Stiftung, Department of Science and Technology, Ministry of Science and Technology, India, Deutsche Forschungsgemeinschaft

OpenAlex ID: W2161392888

FWCI: 8.019

Cites: 39

Cited by: 442

Related to: 10