A Model-based Genetic Programming Approach for Symbolic Regression of Small Expressions

04/03/2019
by   Marco Virgolin, et al.
0

The Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) is a model-based EA framework that has been shown to perform well in several domains, including Genetic Programming (GP). Differently from traditional EAs where variation acts randomly, GOMEA learns a model of interdependencies within the genotype, i.e., the linkage, to estimate what patterns to propagate. In this article, we study the role of Linkage Learning (LL) performed by GOMEA in Symbolic Regression (SR). We show that the non-uniformity in the distribution of the genotype in GP populations negatively biases LL, and propose a method to correct for this. We also propose approaches to improve LL when ephemeral random constants are used. Furthermore, we adapt a scheme of interleaving runs to alleviate the burden of tuning the population size, a crucial parameter for LL, to SR. We run experiments on 10 real-world datasets, enforcing a strict limitation on solution size, to enable interpretability. We find that the new LL method outperforms the standard one, and that GOMEA outperforms both traditional and semantic GP. We also find that the small solutions evolved by GOMEA are competitive with tuned decision trees, making GOMEA a promising new approach to SR.

READ FULL TEXT

page 1

page 5

page 11

research
04/03/2019

Model-based Genetic Programming with GOMEA for Symbolic Regression of Small Expressions

The Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) has been sho...
research
04/26/2022

Coefficient Mutation in the Gene-pool Optimal Mixing Evolutionary Algorithm for Symbolic Regression

Currently, the genetic programming version of the gene-pool optimal mixi...
research
04/18/2023

Differentiable Genetic Programming for High-dimensional Symbolic Regression

Symbolic regression (SR) is the process of discovering hidden relationsh...
research
04/28/2022

Taylor Genetic Programming for Symbolic Regression

Genetic programming (GP) is a commonly used approach to solve symbolic r...
research
09/13/2020

Simple Simultaneous Ensemble Learning in Genetic Programming

Learning ensembles by bagging can substantially improve the generalizati...
research
06/07/2018

GP-RVM: Genetic Programing-based Symbolic Regression Using Relevance Vector Machine

This paper proposes a hybrid basis function construction method (GP-RVM)...
research
01/30/2020

SGP-DT: Semantic Genetic Programming Based on Dynamic Targets

Semantic GP is a promising approach that introduces semantic awareness d...

Please sign up or login with your details

Forgot password? Click here to reset