A Genetic Programming Approach to Solomonoff's Probabilistic Induction

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{eurogp06:DeFalcoDellaCioppaMaistoTarantino,

• author = "Ivanoe {De Falco} and Antonio {Della Cioppa} and Domenico Maisto and Ernesto Tarantino",
• title = "A Genetic Programming Approach to {Solomonoff's} Probabilistic Induction",
• editor = "Pierre Collet and Marco Tomassini and Marc Ebner and Steven Gustafson and Anik\'o Ek\'art",
• booktitle = "Proceedings of the 9th European Conference on Genetic Programming",
• publisher = "Springer",
• series = "Lecture Notes in Computer Science",
• volume = "3905",
• year = "2006",
• address = "Budapest, Hungary",
• month = "10 - 12 " # apr,
• organisation = "EvoNet",
• keywords = "genetic algorithms, genetic programming",
• ISBN = "3-540-33143-3",
• pages = "24--35",
• DOI = "doi:10.1007/11729976_3",
• bibsource = "DBLP, http://dblp.uni-trier.de",
• abstract = "In the context of Solomonoff's Inductive Inference theory, Induction operator plays a key role in modelling and correctly predicting the behaviour of a given phenomenon. Unfortunately, this operator is not algorithmically computable. The present paper deals with a Genetic Programming approach to Inductive Inference, with reference to Solomonoff's algorithmic probability theory, that consists in evolving a population of mathematical expressions looking for the `optimal' one that generates a collection of data and has a maximal a priori probability. Validation is performed on Coulomb's Law, on the Henon series and on the Arosa Ozone time series. The results show that the method is effective in obtaining the analytical expression of the first two problems, and in achieving a very good approximation and forecasting of the third.",
• notes = "Part of \cite{collet:2006:GP} EuroGP'2006 held in conjunction with EvoCOP2006 and EvoWorkshops2006",

}

Genetic Programming entries for Ivanoe De Falco Antonio Della Cioppa Domenico Maisto Ernesto Tarantino

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