Upgrades of Genetic Programming for Data Driven Modelling of Time Series
Created by W.Langdon from
gp-bibliography.bib Revision:1.8028
- @Article{Murari:EC,
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author = "A. Murari and E. Peluso and L. Spolladore and
R. Rossi and M. Gelfusa",
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title = "Upgrades of Genetic Programming for Data Driven
Modelling of Time Series",
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journal = "Evolutionary Computation",
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year = "2023",
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volume = "31",
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number = "4",
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pages = "401--432",
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month = "Winter",
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keywords = "genetic algorithms, genetic programming, Time Series
Analysis, Empirical modeling of signals, Evolutionary
Computation, Symbolic regression, Data Driven Theory",
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ISSN = "1063-6560",
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DOI = "doi:10.1162/evco_a_00330",
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size = "31 pages",
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abstract = "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 modeling 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.",
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notes = "also known as
\cite{DBLP:journals/ec/MurariPSRG23}
online April 28 2023",
- }
Genetic Programming entries for
Andrea Murari
Emmanuele Peluso
Luca Spolladore
Riccardo Rossi
Michela Gelfusa
Citations