Symbolic Regression Driven by Training Data and Prior Knowledge

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F20%3A00344484" target="_blank" >RIV/68407700:21230/20:00344484 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21730/20:00344484

Result on the web

<a href="https://doi.org/10.1145/3377930.3390152" target="_blank" >https://doi.org/10.1145/3377930.3390152</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3377930.3390152" target="_blank" >10.1145/3377930.3390152</a>

Result language

angličtina

Original language name

Symbolic Regression Driven by Training Data and Prior Knowledge

Original language description

In symbolic regression, the search for analytic models is typically driven purely by the prediction error observed on the training data samples. However, when the data samples do not sufficiently cover the input space, the prediction error does not provide sufficient guidance toward desired models. Standard symbolic regression techniques then yield models that are partially incorrect, for instance, in terms of their steady-state characteristics or local behavior. If these properties were considered already during the search process, more accurate and relevant models could be produced. We propose a multi-objective symbolic regression approach that is driven by both the training data and the prior knowledge of the properties the desired model should manifest. The properties given in the form of formal constraints are internally represented by a set of discrete data samples on which candidate models are exactly checked. The proposed approach was experimentally evaluated on three test problems with results clearly demonstrating its capability to evolve realistic models that fit the training data well while complying with the prior knowledge of the desired model characteristics at the same time. It outperforms standard symbolic regression by several orders of magnitude in terms of the mean squared deviation from a reference model.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/EF15_003%2F0000470" target="_blank" >EF15_003/0000470: Robotics 4 Industry 4.0</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2020

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference

ISBN

978-1-4503-7128-5

ISSN

—

e-ISSN

—

Number of pages

9

Pages from-to

958-966

Publisher name

Association for Computing Machinery

Place of publication

New York

Event location

Cancun

Event date

Jul 8, 2020

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000605292300111