Authors: | Beyer, Hans-Georg Deb, Kalyanmoy |
Title: | Self-Adaptive Genetic Algorithms with Simulated Binary Crossover |
Language (ISO): | en |
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 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 simulated binary crossover (SBX) operator and without any mutation operator. The connection between the working of self-adaptive ESs and real-parameter GAs with 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 of emphasizing further studies on self-adaptive GAs. |
URI: | http://hdl.handle.net/2003/5370 http://dx.doi.org/10.17877/DE290R-15294 |
Issue Date: | 2001-10-16 |
Provenance: | Universität Dortmund |
Appears in Collections: | Sonderforschungsbereich (SFB) 531 |
Files in This Item:
File | Description | Size | Format | |
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ci61.pdf | DNB | 563.7 kB | Adobe PDF | View/Open |
ci61.ps | 886.41 kB | Postscript | View/Open |
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