Population subset selection for the use of a validation dataset for overfitting control in genetic programming

Abstract

Genetic Programming (GP) is a technique which is able to solve different problems through the evolution of mathematical expressions. However, in order to be applied, its tendency to overfit the data is one of its main issues. The use of a validation dataset is a common alternative to prevent overfitting in many Machine Learning (ML) techniques, including GP. But, there is one key point which differentiates GP and other ML techniques: instead of training a single model, GP evolves a population of models. Therefore, the use of the validation dataset has several possibilities because any of those evolved models could be evaluated. This work explores the possibility of using the validation dataset not only on the training-best individual but also in a subset with the training-best individuals of the population. The study has been conducted with 5 wellknown databases performing regression or classification tasks. In most of the cases, the results of the study point out to an improvement when the validation dataset is used on a subset of the population instead of only on the training-best individual, which also induces a reduction on the number of nodes and, consequently, a lower complexity on the expressions.

A multigene genetic programming-based fuzzy regression approach for modelling customer satisfaction based on online reviews

“…However, the modelling of CS in previous studies is solely based on survey data. Moreover, the CS models developed based on conventional GP based methods could not adapt to new datasets due to overfitting (Chen et al 2019;Rivero et al 2019). On the other hand, the solutions generated based on chaos-based optimisation could be unstable due to the likelihood of the unstable structures generated for CS models.…”

Rademacher Complexity for Enhancing the Generalization of Genetic Programming for Symbolic Regression

“…How can we select models according to both the training performance and the complexity measure? Researchers have been working on measuring and controlling model complexity in GP for decades [5], [6], [7], [8], [9]. Existing complexity measures in GP can be classified into two groups, which are structural/genotypic complexity measures and behavioural/phenotypic complexity measures.…”