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We propose a new type of leaf node for use in Symbolic Regression (SR) that performs linear combinations of feature variables (LCF). ese nodes can be handled in three dierent modes – an unsynchronized mode, where all LCFs are free to change on their own, a synchronized mode, where LCFs are sorted into groups in which they are forced to be identical(More)
Grammatical Evolution is an algorithm of Genetic Programming but it is capable of evolving programs in an arbitrary language given by a user-provided context-free grammar. We present a way how to apply Multi-Gene idea, known from Multi-Gene Genetic Programming, to Grammatical Evolution, just by modifying the given grammar. We also describe modifications(More)
Genetic Programming has been very successful in solving a large area of problems but its use as a machine learning algorithm has been limited so far. One of the reasons is the problem of overfitting which cannot be solved or suppresed as easily as in more traditional approaches. Another problem, closely related to overfitting, is the selection of the final(More)
We propose a new type of leaf node for use in Symbolic Regression (SR) that performs linear combinations of feature variables (LCF). LCF's weights are tuned using a gradient method based on back-propagation algorithm known from neural networks. Multi-Gene Genetic Programming (MGGP) was chosen as a baseline model. As a sanity check, we experimentally show(More)
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