Autor(en): | Beyer, Hans-Georg Deb, Kalyanmoy |
Titel: | Self-Adaptive Genetic Algorithms with Simulated Binary Crossover |
Sprache (ISO): | en |
Zusammenfassung: | 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 |
Erscheinungsdatum: | 2001-10-16 |
Provinienz: | Universität Dortmund |
Enthalten in den Sammlungen: | Sonderforschungsbereich (SFB) 531 |
Dateien zu dieser Ressource:
Datei | Beschreibung | Größe | Format | |
---|---|---|---|---|
ci61.pdf | DNB | 563.7 kB | Adobe PDF | Öffnen/Anzeigen |
ci61.ps | 886.41 kB | Postscript | Öffnen/Anzeigen |
Diese Ressource ist urheberrechtlich geschützt. |
Diese Ressource ist urheberrechtlich geschützt.
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