Abstract
In this paper we analyze the effects of using nonlinear least squares for parameter identification of symbolic regression models and integrate it as local search mechanism in tree-based genetic programming. We employ the Levenberg–Marquardt algorithm for parameter optimization and calculate gradients via automatic differentiation. We provide examples where the parameter identification succeeds and fails and highlight its computational overhead. Using an extensive suite of symbolic regression benchmark problems we demonstrate the increased performance when incorporating nonlinear least squares within genetic programming. Our results are compared with recently published results obtained by several genetic programming variants and state of the art machine learning algorithms. Genetic programming with nonlinear least squares performs among the best on the defined benchmark suite and the local search can be easily integrated in different genetic programming algorithms as long as only differentiable functions are used within the models.
Highlights
Symbolic regression is the task of finding a mathematical model that best explains the relationship between one or more independent variables and one dependent variable
As a biologically-inspired approach guided by fitness-based selection, the genetic programming (GP) search process for symbolic regression is characterized by a loose coupling between fitness, expressed as an error measure with respect to the target variable, and variation operators | subordinate search heuristics in solution space that generate new models in each generation
It is difficult to foresee the effects on model output when variation operators perform changes on the model structure, often leading to situations where promising model structures are ignored by the algorithm due to low fitness caused by ill-fitting parameters [44]
Summary
Symbolic regression is the task of finding a mathematical model that best explains the relationship between one or more independent variables and one dependent variable. It is difficult to foresee the effects on model output when variation operators perform changes on the model structure, often leading to situations where promising model structures are ignored by the algorithm due to low fitness caused by ill-fitting parameters [44]. In some cases, this can lead to necessary building blocks becoming extinct in the population before they are combined in a solution and recognized by the algorithm.
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