Analytic Continued Fractions for Regression: A Memetic Algorithm Approach

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@Article{MOSCATO:2021:ESA,

• author = "Pablo Moscato and Haoyuan Sun and Mohammad Nazmul Haque",
• title = "Analytic Continued Fractions for Regression: A Memetic Algorithm Approach",
• journal = "Expert Systems with Applications",
• volume = "179",
• pages = "115018",
• year = "2021",
• keywords = "genetic algorithms, genetic programming, Symbolic regression, Memetic algorithm, Analytic continued fractions",
• ISSN = "0957-4174",
• URL = "https://arxiv.org/abs/2001.00624",
• URL = "https://www.sciencedirect.com/science/article/pii/S0957417421004590",
• DOI = "doi:10.1016/j.eswa.2021.115018",
• abstract = "We present an approach for regression problems that employs analytic continued fractions as a novel representation. Comparative computational results using a memetic algorithm are reported in this work. Our experiments included fifteen other different machine learning approaches including five genetic programming methods for symbolic regression and ten machine learning methods. The comparison on training and test generalization was performed using 94 datasets of the Penn State Machine Learning Benchmark. The statistical tests showed that the generalization results using analytic continued fractions provide a powerful and interesting new alternative in the quest for compact and interpretable mathematical models for artificial intelligence",

}

Genetic Programming entries for Pablo Moscato Haoyuan Sun Mohammad Nazmul Haque

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