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Erschienen in:
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2020 | OriginalPaper | Buchkapitel

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

verfasst von : Miguel Nicolau, James McDermott

Erschienen in: Genetic Programming Theory and Practice XVII

Verlag: Springer International Publishing

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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.

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Fußnoten
1
In many GP configurations, there are no true local optima since the mutation operator can jump to anywhere in the search space in single step. We can informally define a local pseudo-optimum as a point where improving steps are not impossible but highly unlikely.
 
2
We adopt the whisker definition of 3rd-quantile +  1.5 * IQR for the upper whisker, and inversely for the lower whisker.
 
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Metadaten
Titel
Genetic Programming Symbolic Regression: What Is the Prior on the Prediction?
verfasst von
Miguel Nicolau
James McDermott
Copyright-Jahr
2020
Buch
Genetic Programming Theory and Practice XVII

Print ISBN: 978-3-030-39957-3

Electronic ISBN: 978-3-030-39958-0

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-39958-0_11

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