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http://dx.doi.org/10.14400/JDC.2020.18.12.481

Symbolic regression based on parallel Genetic Programming  

Kim, Chansoo (Department of Applied Mathematics, Kongju National University)
Han, Keunhee (Department of Applied Mathematics, Kongju National University)
Publication Information
Journal of Digital Convergence / v.18, no.12, 2020 , pp. 481-488 More about this Journal
Abstract
Symbolic regression is an analysis method that directly generates a function that can explain the relationsip between dependent and independent variables for a given data in regression analysis. Genetic Programming is the leading technology of research in this field. It has the advantage of being able to directly derive a model that can be interpreted compared to other regression analysis algorithms that seek to optimize parameters from a fixed model. In this study, we propse a symbolic regression algorithm using parallel genetic programming based on a coarse grained parallel model, and apply the proposed algorithm to PMLB data to analyze the effectiveness of the algorithm.
Keywords
Symbolic Regression; Genetic Algorithmj; Genetic Programming; Parallel Model; PMLB Data;
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