On the elusivity of dynamic optimisation problems

Alza, Joan; Bartlett, Mark; Ceberio, Josu; McCall, John

doi:10.1016/j.swevo.2023.101289

Cited by 1 publication

(1 citation statement)

References 51 publications

(124 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, research has indicated that simplistic, simple restarts of the optimizer or plainly specialized optimization algorithms might be sufficient to solve many dynamic optimization problems [36]. We therefore include a GA geared toward static optimization problems in our analysis.…”

Section: Open-ended Evolutionary Algorithmsmentioning

confidence: 99%

Population Dynamics in Genetic Programming for Dynamic Symbolic Regression

Fleck,

Werth,

Affenzeller

2024

Applied Sciences

View full text Add to dashboard Cite

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 analyzes 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.

show abstract

Section: Open-ended Evolutionary Algorithmsmentioning

confidence: 99%