Feature Extraction for Surrogate Models in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7642

@InProceedings{Pilat:2016:PPSN,

• author = "Martin Pilat and Roman Neruda",
• title = "Feature Extraction for Surrogate Models in Genetic Programming",
• booktitle = "14th International Conference on Parallel Problem Solving from Nature",
• year = "2016",
• editor = "Julia Handl and Emma Hart and Peter R. Lewis and Manuel Lopez-Ibanez and Gabriela Ochoa and Ben Paechter",
• volume = "9921",
• series = "LNCS",
• pages = "335--344",
• address = "Edinburgh",
• month = "17-21 " # sep,
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, Surrogate model, Random forest",
• isbn13 = "978-3-319-45823-6",
• DOI = "doi:10.1007/978-3-319-45823-6_31",
• abstract = "We discuss the use of surrogate models in the field of genetic programming. We describe a set of features extracted from each tree and use it to train a model of the fitness function. The results indicate that such a model can be used to predict the fitness of new individuals without the need to evaluate them. In a series of experiments, we show how surrogate modelling is able to reduce the number of fitness evaluations needed in genetic programming, and we discuss how the use of surrogate models affects the exploration and convergence of genetic programming algorithms.",
• notes = "PPSN2016 http://ppsn2016.org",

}

Genetic Programming entries for Martin Pilat Roman Neruda

Citations