Applying Boosting Techniques to Genetic Programming

Authors:
Gregory Paris;Denis Robilliard;Cyril Fonlupt
Affiliations:
-;-;-
Venue:
Selected Papers from the 5th European Conference on Artificial Evolution
Year:
2001

Citing 9
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Abstract

This article deals with an improvement for genetic programming based on a technique originating from the machine learning field: boosting. In a first part of this paper, we test the improvements offered by boosting on binary problems. Then we propose to deal with regression problems, and propose an algorithm, called GPboost, that keeps closer to the original idea of distribution in Adaboost than what has been done in previous implementation of boosting for genetic programming.