Population Dynamics in Genetic Programming for Dynamic Symbolic Regression
Philipp Fleck,
Bernhard Werth,
Michael Affenzeller
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 an… Show more
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