Applying Boosting Techniques to Genetic Programming

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@InProceedings{paris:2001:EA,

• author = "Gregory Paris and Denis Robilliard and Cyril Fonlupt",
• title = "Applying Boosting Techniques to Genetic Programming",
• booktitle = "Artificial Evolution 5th International Conference, Evolution Artificielle, EA 2001",
• year = "2001",
• editor = "P. Collet and C. Fonlupt and J.-K. Hao and E. Lutton and M. Schoenauer",
• volume = "2310",
• series = "LNCS",
• pages = "267--278",
• address = "Creusot, France",
• month = oct # " 29-31",
• publisher = "Springer Verlag",
• ISBN = "3-540-43544-1",
• URL = "http://www-lil.univ-littoral.fr/~fonlupt/Recherche/Publi/ea01-2.ps.gz",
• DOI = "doi:10.1007/3-540-46033-0_22",
• keywords = "genetic algorithms, genetic programming",
• 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.",
• notes = "EA'01",

}

Genetic Programming entries for Gregory Paris Denis Robilliard Cyril Fonlupt

Citations