Zoetrope Genetic Programming for Regression

Aurélie Boisbunon (1) , Carlo Fanara (1) , Ingrid Grenet (1) , Jonathan Daeden (1) , Alexis Vighi (1) , Marc Schoenauer (2)

1 MyDataModels
2 TAU - TAckling the Underspecified

The Zoetrope Genetic Programming (ZGP) algorithm is based on an original representation for mathematical expressions, targeting evolutionary symbolic regression. The zoetropic representation uses repeated fusion operations between partial expressions, starting from the terminal set. Repeated fusions within an individual gradually generate more complex expressions, ending up in what can be viewed as new features. These features are then linearly combined to best fit the training data. ZGP individuals then undergo specific crossover and mutation operators, and selection takes place between parents and offspring. ZGP is validated using a large number of public domain regression datasets, and compared to other symbolic regression algorithms, as well as to traditional machine learning algorithms. ZGP reaches state-of-theart performance with respect to both types of algorithms, and demonstrates a low computational time compared to other symbolic regression approaches.

Domaines: Machine Learning [stat.ML]; Intelligence artificielle [cs.AI]

Aurélie Boisbunon, Carlo Fanara, Ingrid Grenet, Jonathan Daeden, Alexis Vighi, et al.. Zoetrope Genetic Programming for Regression. GECCO 2021, ACM, Jul 2021, Lille, France. pp.776-784. ⟨hal-03155694⟩