- author = "Lukas Kammerer and Gabriel Kronberger and Stephan Winkler",
- title = "Empirical Analysis of Variance for Genetic Programming based Symbolic Regression",
- booktitle = "Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion",
- year = "2021",
- editor = "Francisco Chicano and Alberto Tonda and Krzysztof Krawiec and Marde Helbig and Christopher W. Cleghorn and Dennis G. Wilson and Georgios Yannakakis and Luis Paquete and Gabriela Ochoa and Jaume Bacardit and Christian Gagne and Sanaz Mostaghim and Laetitia Jourdan and Oliver Schuetze and Petr Posik and Carlos Segura and Renato Tinos and Carlos Cotta and Malcolm Heywood and Mengjie Zhang and Leonardo Trujillo and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Fuyuki Ishikawa and Inmaculada Medina-Bulo and Frank Neumann and Andrew M. Sutton",
- pages = "251--252",
- address = "internet",
- series = "GECCO '21",
- month = jul # " 10-14",
- organisation = "SIGEVO",
- publisher = "Association for Computing Machinery",
- publisher_address = "New York, NY, USA",
- keywords = "genetic algorithms, genetic programming, Symbolic Regression, Bias/Variance Tradeoff: Poster",
- isbn13 = "978-1-4503-8351-6",
-
DOI = "
doi:10.1145/3449726.3459486",
- size = "2 pages",
-
abstract = "Genetic programming (GP) based symbolic regression is
a stochastic, high-variance algorithm. Its sensitivity
to changes in training data is a drawback for practical
applications.
we analyze empirically the variance of GP models on the PennML benchmarks. We measure the spread of model predictions when models are trained on slightly perturbed data. We compare the spread of models from two GP variants as well as linear, polynomial and random forest regression models.
The results show that the spread of models from GP with local optimization is significantly higher than that of all other algorithms.As a side effect of our analysis, we provide evidence that the PennML benchmark contains two groups of instances (Friedman and real-world problem instances) for which GP performs significantly different",
-
notes = "
GECCO-2021 A Recombination of the 30th International Conference on Genetic Algorithms (ICGA) and the 26th Annual Genetic Programming Conference (GP)",
