Population Dynamics in Genetic Programming for Dynamic Symbolic Regression
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- @Article{fleck:2024:AS,
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author = "Philipp Fleck and Bernhard Werth and
Michael Affenzeller",
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title = "Population Dynamics in Genetic Programming for Dynamic
Symbolic Regression",
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journal = "Applied Sciences",
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year = "2024",
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volume = "14",
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number = "2",
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pages = "Article No. 596",
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keywords = "genetic algorithms, genetic programming",
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ISSN = "2076-3417",
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URL = "https://www.mdpi.com/2076-3417/14/2/596",
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DOI = "doi:10.3390/app14020596",
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abstract = "This paper investigates the application of genetic
programming (GP) for dynamic symbolic regression (SR),
addressing the challenge of adapting machine learning
models to evolving data in practical applications.
Benchmark instances with changing underlying functions
over time are defined to assess the performance of a
genetic algorithm (GA) as a traditional evolutionary
algorithm and an age-layered population structure
(ALPS) as an open-ended evolutionary algorithm for
dynamic symbolic regression. This study analyses
population dynamics by examining variable frequencies
and impact changes over time in response to dynamic
shifts in the training data. The results demonstrate
the effectiveness of both the GA and ALPS in handling
changing data, showcasing their ability to recover and
evolve improved solutions after an initial drop in
population quality following data changes. Population
dynamics reveal that variable impacts respond rapidly
to data changes, while variable frequencies shift
gradually across generations, aligning with the
indirect measure of fitness represented by variable
impacts. Notably, the GA shows a strong dependence on
mutation to avoid variables becoming permanently
extinct, contrasting with the ALPS's unexpected
insensitivity to mutation rates owing to its reseeding
mechanism for effective variable reintroduction.",
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notes = "also known as \cite{app14020596}",
- }
Genetic Programming entries for
Philipp Fleck
Bernhard Werth
Michael Affenzeller
Citations