Upgrades of Genetic Programming for Data Driven Modelling of Time Series

In many engineering fields and scientific disciplines, the results of experiments are in the form of time series, which can be quite problematic to interpret and model. Genetic programming tools are quite powerful in extracting knowledge from data. In this work, several upgrades and refinements are...

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Veröffentlicht in:Evolutionary computation Jg. 31; H. 4; S. 1
Hauptverfasser: Murari, A, Peluso, E, Spolladore, L, Rossi, R, Gelfusa, M
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States 01.12.2023
Schlagworte:
Data Driven Theory
Symbolic regression
Evolutionary Computation
Empirical modelling of signals
Genetic Programming
Time Series Analysis
ISSN:1530-9304, 1530-9304
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Zusammenfassung:In many engineering fields and scientific disciplines, the results of experiments are in the form of time series, which can be quite problematic to interpret and model. Genetic programming tools are quite powerful in extracting knowledge from data. In this work, several upgrades and refinements are proposed and tested to improve the explorative capabilities of Symbolic Regression (SR) via Genetic Programming (GP) for the investigation of time series, with the objective of extracting mathematical models directly from the available signals. The main task is not simply prediction but consists of identifying interpretable equations, reflecting the nature of the mechanisms generating the signals. The implemented improvements involve almost all aspects of GP, from the knowledge representation and the genetic operators to the fitness function. The unique capabilities of genetic programming, to accommodate prior information and knowledge, are also leveraged effectively. The proposed upgrades cover the most important applications of empirical modelling of time series, ranging from the identification of autoregressive systems and partial differential equations to the search of models in terms of dimensionless quantities and appropriate physical units. Particularly delicate systems to identify, such as those showing hysteretic behaviour or governed by delayed differential equations, are also addressed. The potential of the developed tools is substantiated with both a battery of systematic numerical tests with synthetic signals and with applications to experimental data.
Bibliographie:ObjectType-Article-1
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ISSN:1530-9304
1530-9304
DOI:10.1162/evco_a_00330