Deb, Kalyanmoy ; Beyer, Hans-Georg (2001) Self-adaptive genetic algorithms with simulated binary crossover Evolutionary Computation, 9 (2). pp. 197-221. ISSN 1063-6560
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Official URL: http://www.mitpressjournals.org/doi/abs/10.1162/10...
Related URL: http://dx.doi.org/10.1162/106365601750190406
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.
Item Type: | Article |
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Source: | Copyright of this article belongs to MIT Press. |
ID Code: | 9415 |
Deposited On: | 02 Nov 2010 12:15 |
Last Modified: | 16 May 2016 19:13 |
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