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Generic drift in genetic algorithm selection schemes
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AI
DOI：10.1109/4235.797972.png)
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Abstract
En 中文
A method for calculating genetic drift in terms of changing population fitness variance is presented. The method allows for an easy comparison of different selection schemes and exact analytical results are derived for traditional generational selection, steady-state selection with varying generation gap, a simple model of Eshelman's CBC algorithm, and (mu + lambda) evolution strategies. The effects of changing genetic drift on the convergence of a GA are demonstrated empirically.
Keywords:
evolution strategy
genetic algorithm
genetic drift
selection operator
Journal
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12
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1.9K
Citations:
2.4W
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