Diversity control in GP with ADF for regression tasks

Authors:
Huayang Xie
Affiliations:
School of Mathematics, Statistics, and Computer Science, Victoria University of Wellington, Wellington, New Zealand
Venue:
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Year:
2005

Citing 3
Cited 3

Genetic programming: on the programming of computers by means of natural selection

Genetic programming: on the programming of computers by means of natural selection
Pygmies and civil servants

Advances in genetic programming
The Effect of Extensive Use of the Mutation Operator on Generalization in Genetic Programming Using Sparse Data Sets

PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature

A survey and taxonomy of performance improvement of canonical genetic programming

Knowledge and Information Systems
A review of tournament selection in genetic programming

ISICA'10 Proceedings of the 5th international conference on Advances in computation and intelligence
Population clustering in genetic programming

EuroGP'06 Proceedings of the 9th European conference on Genetic Programming

Quantified Score

Hi-index	0.03

Visualization

Abstract

This paper proposes a two-phase diversity control approach to prevent the common problem of the loss of diversity in Genetic Programming with Automatically Defined Functions. While most recent work focuses on diagnosing and remedying the loss of diversity, this approach aims to prevent the loss of diversity in the early stage through a refined diversity control method and a fully covered tournament selection method. The results on regression tasks suggest that these methods can effectively improve the system performance by reducing the incidences of premature convergence and the number of generations needed for finding an optimal solution.