Inference of compact nonlinear dynamic models by epigenetic local search
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{LaCava:2016:EAAI,
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author = "William {La Cava} and Kourosh Danai and Lee Spector",
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title = "Inference of compact nonlinear dynamic models by
epigenetic local search",
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journal = "Engineering Applications of Artificial Intelligence",
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year = "2016",
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volume = "55",
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pages = "292--306",
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month = oct,
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keywords = "genetic algorithms, genetic programming, System
identification, Dynamical systems, Differential
equations, Symbolic regression",
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ISSN = "0952-1976",
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URL = "http://www.sciencedirect.com/science/article/pii/S0952197616301294",
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DOI = "doi:10.1016/j.engappai.2016.07.004",
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abstract = "We introduce a method to enhance the inference of
meaningful dynamic models from observational data by
genetic programming (GP). This method incorporates an
inheritable epigenetic layer that specifies active and
inactive genes for a more effective local search of the
model structure space. We define several GP
implementations using different features of
epigenetics, such as passive structure, phenotypic
plasticity, and inheritable gene regulation. To test
these implementations, we use hundreds of data sets
generated from nonlinear ordinary differential
equations (ODEs) in several fields of engineering and
from randomly constructed nonlinear ODE models. The
results indicate that epigenetic hill climbing
consistently produces more compact dynamic equations
with better fitness values, and that it identifies the
exact solution of the system more often, validating the
categorical improvement of GP by epigenetic local
search. The results further indicate that when faced
with complex dynamics, epigenetic hill climbing reduces
the computational effort required to infer the correct
underlying dynamics. We then apply the method to the
identification of three real-world systems: a cascaded
tanks system, a chemical distillation tower, and an
industrial wind turbine. We analyse its solutions in
comparison to theoretical and black-box approaches in
terms of accuracy and intelligibility. Finally, we
analyze population homology to evaluate the efficiency
of the method. The results indicate that the epigenetic
implementations provide protection from premature
convergence by maintaining diversity in silenced
portions of programs.",
- }
Genetic Programming entries for
William La Cava
Kourosh Danai
Lee Spector
Citations