Zobrazeno 1 - 10
of 38
pro vyhledávání: '"de França, Fabricio Olivetti"'
Automatically creating a computer program using input-output examples can be a challenging task, especially when trying to synthesize computer programs that require loops or recursion. Even though the use of recursion can make the algorithmic descrip
Externí odkaz:
http://arxiv.org/abs/2406.01500
Autor:
Kronberger, Gabriel, de Franca, Fabricio Olivetti, Desmond, Harry, Bartlett, Deaglan J., Kammerer, Lukas
We analyse the search behaviour of genetic programming for symbolic regression in practically relevant but limited settings, allowing exhaustive enumeration of all solutions. This enables us to quantify the success probability of finding the best pos
Externí odkaz:
http://arxiv.org/abs/2404.17292
Publikováno v:
GSI Aldeia, FO de Fran\c{c}a, and WG La Cava. 2024. Minimum variance threshold for epsilon-lexicase selection. In Genetic and Evolutionary Computation Conference (GECCO '24)
Parent selection plays an important role in evolutionary algorithms, and many strategies exist to select the parent pool before breeding the next generation. Methods often rely on average error over the entire dataset as a criterion to select the par
Externí odkaz:
http://arxiv.org/abs/2404.05909
Publikováno v:
Aldeia, G.S.I., de Franca, F.O. Interpretability in symbolic regression: a benchmark of explanatory methods using the Feynman data set. Genet Program Evolvable Mach 23, 309-349 (2022)
In some situations, the interpretability of the machine learning models plays a role as important as the model accuracy. Interpretability comes from the need to trust the prediction model, verify some of its properties, or even enforce them to improv
Externí odkaz:
http://arxiv.org/abs/2404.05908
Publikováno v:
GSI Aldeia, FO de Fran\c{c}a, WG La Cava. 2024. Inexact Simplification of Symbolic Regression Expressions with Locality-sensitive Hashing. In Genetic and Evolutionary Computation Conference (GECCO '24)
Symbolic regression (SR) searches for parametric models that accurately fit a dataset, prioritizing simplicity and interpretability. Despite this secondary objective, studies point out that the models are often overly complex due to redundant operati
Externí odkaz:
http://arxiv.org/abs/2404.05898
Program synthesis with Genetic Programming searches for a correct program that satisfies the input specification, which is usually provided as input-output examples. One particular challenge is how to effectively handle loops and recursion avoiding p
Externí odkaz:
http://arxiv.org/abs/2402.13828
Autor:
Russeil, Etienne, de França, Fabrício Olivetti, Malanchev, Konstantin, Burlacu, Bogdan, Ishida, Emille E. O., Leroux, Marion, Michelin, Clément, Moinard, Guillaume, Gangler, Emmanuel
Symbolic regression (SR) searches for analytical expressions representing the relationship between a set of explanatory and response variables. Current SR methods assume a single dataset extracted from a single experiment. Nevertheless, frequently, t
Externí odkaz:
http://arxiv.org/abs/2402.04298
Program synthesis is the process of generating a computer program following a set of specifications, which can be a high-level description of the problem and/or a set of input-output examples. The synthesis can be modeled as a search problem in which
Externí odkaz:
http://arxiv.org/abs/2304.03200
Symbolic regression is a nonlinear regression method which is commonly performed by an evolutionary computation method such as genetic programming. Quantification of uncertainty of regression models is important for the interpretation of models and f
Externí odkaz:
http://arxiv.org/abs/2209.06454
Autor:
de Franca, Fabricio Olivetti
Symbolic Regression searches for a function form that approximates a dataset often using Genetic Programming. Since there is usually no restriction to what form the function can have, Genetic Programming may return a hard to understand model due to n
Externí odkaz:
http://arxiv.org/abs/2205.06807