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.
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Paris, G., Robilliard, D., Fonlupt, C. (2003). Genetic Programming with Boosting for Ambiguities in Regression Problems. In: Ryan, C., Soule, T., Keijzer, M., Tsang, E., Poli, R., Costa, E. (eds) Genetic Programming. EuroGP 2003. Lecture Notes in Computer Science, vol 2610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36599-0_17
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DOI: https://doi.org/10.1007/3-540-36599-0_17
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