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The presented work outlines a new genetic crossover operator, which can be used to solve problems by the Grammatical Evolution technique. This new operator intensively applies the one-point crossover procedure to randomly selected chromosomes with the aim of drastically reducing their fitness value. The new operator is applied to chromosomes selected randomly from the genetic population. This new operator was applied to two techniques from the recent literature that exploit Grammatical Evolution: artificial neural network construction and rule construction. In both case studies, an extensive set of classification problems and data-fitting problems were incorporated to estimate the effectiveness of the proposed genetic operator. The proposed operator significantly reduced both the classification error on the classification datasets and the feature learning error on the fitting datasets compared to other machine learning techniques and also to the original models before applying the new operator. [ABSTRACT FROM AUTHOR]