Differential evolution driven analytic programming for prediction

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F17%3A63517239" target="_blank" >RIV/70883521:28140/17:63517239 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27240/17:10238520 RIV/61989100:27740/17:10238520

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-59060-8_61" target="_blank" >http://dx.doi.org/10.1007/978-3-319-59060-8_61</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-59060-8_61" target="_blank" >10.1007/978-3-319-59060-8_61</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Differential evolution driven analytic programming for prediction

Popis výsledku v původním jazyce

This research deals with the hybridization of symbolic regression open framework, which is Analytical Programming (AP) and Differential Evolution (DE) algorithm in the task of time series prediction. This paper provides a closer insight into applicability and performance of connection between AP and different strategies of DE. AP can be considered as powerful open framework for symbolic regression thanks to its applicability in any programming language with arbitrary driving evolutionary/swarm based algorithm. Thus, the motivation behind this research, is to explore and investigate the differences in performance of AP driven by basic canonical strategies of DE as well as by the state of the art strategy, which is Success-History based Adaptive Differential Evolution (SHADE). Simple experiment has been carried out here with the time series consisting of 300 data-points of GBP/USD exchange rate, where the first 2/3 of data were used for regression process and the last 1/3 of the data were used as a verification for prediction process. The differences between regression/prediction models synthesized by means of AP as a direct consequences of different DE strategies performances are briefly discussed within conclusion section of this paper.

Název v anglickém jazyce

Differential evolution driven analytic programming for prediction

Popis výsledku anglicky

This research deals with the hybridization of symbolic regression open framework, which is Analytical Programming (AP) and Differential Evolution (DE) algorithm in the task of time series prediction. This paper provides a closer insight into applicability and performance of connection between AP and different strategies of DE. AP can be considered as powerful open framework for symbolic regression thanks to its applicability in any programming language with arbitrary driving evolutionary/swarm based algorithm. Thus, the motivation behind this research, is to explore and investigate the differences in performance of AP driven by basic canonical strategies of DE as well as by the state of the art strategy, which is Success-History based Adaptive Differential Evolution (SHADE). Simple experiment has been carried out here with the time series consisting of 300 data-points of GBP/USD exchange rate, where the first 2/3 of data were used for regression process and the last 1/3 of the data were used as a verification for prediction process. The differences between regression/prediction models synthesized by means of AP as a direct consequences of different DE strategies performances are briefly discussed within conclusion section of this paper.

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

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

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

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ISBN

978-3-319-59059-2

ISSN

0302-9743

e-ISSN

neuvedeno

Počet stran výsledku

12

Strana od-do

676-687

Název nakladatele

Springer-Verlag Berlin

Místo vydání

Heidelberg

Místo konání akce

Zakopane

Datum konání akce

11. 6. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000426206100061