Abstract:
In the context of functions optimization, self-adaptation
features of evolutionary search algorithms have been explored only with
evolution strategies (ES) and evolutionary programming (EP). In this paper,
we demonstrate the self-adaptive feature of real-parameter genetic algorithms
(GAs) using the simulated binary crossover (SBX) operator. The connection
between the working of self-adaptive ESs and real-parameter GAs with SBX
operator is also discussed. 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 of real-parameter
GAs and self-adaptive ESs shown in the study suggests the need of emphasizing
further studies on self-adaptive GAs.
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