Navegando por Palavras-chave "Symbolic regression"

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    Automatic feature engineering for regression models with machine learning: An evolutionary computation and statistics hybrid
    (Elsevier Science Inc, 2018) de Melo, Vinicius Veloso [UNIFESP]; Banzhaf, Wolfgang
    Symbolic Regression (SR) is a well-studied task in Evolutionary Computation (EC), where adequate free-form mathematical models must be automatically discovered from observed data. Statisticians, engineers, and general data scientists still prefer traditional regression methods over EC methods because of the solid mathematical foundations, the interpretability of the models, and the lack of randomness, even though such deterministic methods tend to provide lower quality prediction than stochastic EC methods. On the other hand, while EC solutions can be big and uninterpretable, they can be created with less bias, finding high-quality solutions that would be avoided by human researchers. Another interesting possibility is using EC methods to perform automatic feature engineering for a deterministic regression method instead of evolving a single model
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    Studying bloat control and maintenance of effective code in linear genetic programming for symbolic regression
    (Elsevier Science Bv, 2016) dal Piccol Sotto, Leo Francoso [UNIFESP]; de Melo, Vinicius Veloso [UNIFESP]
    Linear Genetic Programming (LGP) is an Evolutionary Computation algorithm, inspired in the Genetic Programming (GP) algorithm. Instead of using the standard tree representation of GP, LGP evolves a linear program, which causes a graph-based data flow with code reuse. LGP has been shown to outperform GP in several problems, including Symbolic Regression (SReg), and to produce simpler solutions. In this paper, we propose several LGP variants and compare them with a traditional LGP algorithm on a set of benchmark SReg functions from the literature. The main objectives of the variants were to both control bloat and privilege useful code in the population. Here we evaluate their effects during the evolution process and in the quality of the final solutions. Analysis of the results showed that bloat control and effective code maintenance worked, but they did not guarantee improvement in solution quality. (C) 2015 Elsevier B.V. All rights reserved.