Symbolic Regression Driven by Training Data and Prior Knowledge

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/68407700:21730/20:00344484

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Symbolic Regression Driven by Training Data and Prior Knowledge

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Symbolic Regression Driven by Training Data and Prior Knowledge

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/EF15_003%2F0000470" target="_blank" >EF15_003/0000470: Robotika pro Průmysl 4.0</a><br>

Návaznosti

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

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

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

ISBN

978-1-4503-7128-5

ISSN

—

e-ISSN

—

Počet stran výsledku

9

Strana od-do

958-966

Název nakladatele

Association for Computing Machinery

Místo vydání

New York

Místo konání akce

Cancun

Datum konání akce

8. 7. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000605292300111