Genetic programming with boosting for ambiguities in regression problems

Authors:
Grégory Paris;Denis Robilliard;Cyril Fonlupt
Affiliations:
Université du Littoral-Côte d'Opale, LIL, Calais Cedex, France;Université du Littoral-Côte d'Opale, LIL, Calais Cedex, France;Université du Littoral-Côte d'Opale, LIL, Calais Cedex, France
Venue:
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Year:
2003

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Abstract

Facing ambiguities in regression problems is a challenge. There exists many powerful evolutionary schemes to deal with regression, however, these techniques do not usually take into account ambiguities (i.e. the existence of 2 or more solutions for some or all points in the domain). Nonetheless ambiguities are present in some real world inverse problems, and it is interesting in such cases to provide the user with a choice of possible solutions. We propose in this article an approach based on boosted genetic programming in order to propose several solutions when ambiguities are detected.