Classification Algorithm With Lexicase Selection


The hybrid fuzzy genetic based algorithm was implemented. The genetic fuzzy systems are applied for classifier learning, and although they allow creating interpretable rule bases, the process of designing a rule base can be improved with specific genetic operators, such as lexicase selection. The influence of lexicase selection on the efficiency of its work for data classification is examined in this article. “Ring”, “Phoneme” and “Satimage” datasets were used for verification. The results were analyzed by verification using Mann-Whitney U test. According to the results of basic hybrid fuzzy genetic based algorithm with lexicase selection of several runs, where in each the duration of the search for the best rule base was limited to five hundred generations, efficiency was only on number of rules, but paired with the previously developed initialization modification, the accuracy and F-score were improved on “Satimage” dataset and the number of rules decreased on all of the datasets.

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27 February 2023

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European Publisher



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1st Edition




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Pleshkova, T., & Stanovov, V. (2023). Classification Algorithm With Lexicase Selection. In P. Stanimorovic, A. A. Stupina, E. Semenkin, & I. V. Kovalev (Eds.), Hybrid Methods of Modeling and Optimization in Complex Systems, vol 1. European Proceedings of Computers and Technology (pp. 194-200). European Publisher.