Improving Gene Expression Programming Performance by Using Differential Evolution

Authors:
Qiongyun Zhang;Chi Zhou;Weimin Xiao;Peter C. Nelson
Affiliations:
-;-;-;-
Venue:
ICMLA '07 Proceedings of the Sixth International Conference on Machine Learning and Applications
Year:
2007

Citing 0
Cited 3

Using differential evolution for symbolic regression and numerical constant creation

Proceedings of the 10th annual conference on Genetic and evolutionary computation
Differential evolution of constants in genetic programming improves efficacy and bloat

Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Effects of constant optimization by nonlinear least squares minimization in symbolic regression

Proceedings of the 15th annual conference companion on Genetic and evolutionary computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

Gene Expression Programming (GEP) is an evolutionary algorithm that incorporates both the idea of a simple, linear chromosome of fixed length used in Genetic Algorithms (GAs) and the tree structure of different sizes and shapes used in Genetic Programming (GP). As with other GP algorithms, GEP has difficulty finding appropriate numeric constants for terminal nodes in the expression trees. In this work, we describe a new approach of constant generation using Differential Evolution (DE), a real-valued GA robust and efficient at parameter optimization. Our experimental results on two symbolic regression problems show that the approach significantly improves the performance of the GEP algorithm. The proposed approach can be easily extended to other Genetic Programming variations.