Maryam Amir Haeri

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This paper introduces a new method that improves the generalization ability of genetic programming (GP) for symbolic regression problems, named variance-based layered learning GP. In this approach, several datasets, called primitive training sets, are derived from the original training data. They are generated from less complex to more complex, for a(More)
Mutual Information (MI) is an important dependency measure between random variables, due to its tight connection with information theory. It has numerous applications, both in theory and practice. However, when employed in practice, it is often necessary to estimate the MI from available data. There are several methods to approximate the MI, but arguably(More)
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