Genetic Programming Symbolic Regression: What Is the Prior on the Prediction?

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

@InProceedings{Nicolau:2019:GPTP,

• author = "Miguel Nicolau and James McDermott",
• title = "Genetic Programming Symbolic Regression: What Is the Prior on the Prediction?",
• booktitle = "Genetic Programming Theory and Practice XVII",
• year = "2019",
• editor = "Wolfgang Banzhaf and Erik Goodman and Leigh Sheneman and Leonardo Trujillo and Bill Worzel",
• pages = "201--225",
• address = "East Lansing, MI, USA",
• month = "16-19 " # may,
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-3-030-39957-3",
• DOI = "doi:10.1007/978-3-030-39958-0_11",
• abstract = "In the context of Genetic Programming Symbolic Regression, we empirically investigate the prior on the output prediction, that is, the distribution of the output prior to observing data. We distinguish between the prior due to initialisation and due to evolutionary search. We also investigate the effect on the prior of maximum tree depth and the effect of different function sets and different independent variable distributions. We find that priors are highly diffuse and sometimes include support for extreme values. We compare priors to values for dependent variables observed in benchmarks and real-world problems, finding that mismatches occur and can affect algorithm behaviour and performance. As a further application of our results, we investigate the behaviour of mutation operators in semantic space.",
• notes = "Part of \cite{Banzhaf:2019:GPTP}, published after the workshop",

}

Genetic Programming entries for Miguel Nicolau James McDermott

Citations