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pro vyhledávání: '"Kammerer, Lukas"'
Autor:
Kronberger, Gabriel, de Franca, Fabricio Olivetti, Desmond, Harry, Bartlett, Deaglan J., Kammerer, Lukas
We analyse the search behaviour of genetic programming for symbolic regression in practically relevant but limited settings, allowing exhaustive enumeration of all solutions. This enables us to quantify the success probability of finding the best pos
Externí odkaz:
http://arxiv.org/abs/2404.17292
Autor:
Bartlett, Deaglan J., Kammerer, Lukas, Kronberger, Gabriel, Desmond, Harry, Ferreira, Pedro G., Wandelt, Benjamin D., Burlacu, Bogdan, Alonso, David, Zennaro, Matteo
Publikováno v:
A&A 686, A209 (2024)
Computing the matter power spectrum, $P(k)$, as a function of cosmological parameters can be prohibitively slow in cosmological analyses, hence emulating this calculation is desirable. Previous analytic approximations are insufficiently accurate for
Externí odkaz:
http://arxiv.org/abs/2311.15865
Publikováno v:
In: Moreno-Diaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory EUROCAST 2022. Lecture Notes in Computer Science, vol 13789. Springer, Cham
Fast Function Extraction (FFX) is a deterministic algorithm for solving symbolic regression problems. We improve the accuracy of FFX by adding parameters to the arguments of nonlinear functions. Instead of only optimizing linear parameters, we optimi
Externí odkaz:
http://arxiv.org/abs/2209.09675
Autor:
Kronberger, Gabriel, Kammerer, Lukas, Burlacu, Bogdan, Winkler, Stephan M., Kommenda, Michael, Affenzeller, Michael
Publikováno v:
eIn: Banzhaf W. et al (eds) Genetic Programming Theory and Practice XVI. Genetic and Evolutionary Computation. Springer, Cham. pp 85-102 (2019)
In this chapter we take a closer look at the distribution of symbolic regression models generated by genetic programming in the search space. The motivation for this work is to improve the search for well-fitting symbolic regression models by using i
Externí odkaz:
http://arxiv.org/abs/2109.13898
Autor:
Kammerer, Lukas, Kronberger, Gabriel, Burlacu, Bogdan, Winkler, Stephan M., Kommenda, Michael, Affenzeller, Michael
Publikováno v:
In: Banzhaf W. et al (eds) Genetic Programming Theory and Practice XVII, pp 79-99 (2020)
Symbolic regression is a powerful system identification technique in industrial scenarios where no prior knowledge on model structure is available. Such scenarios often require specific model properties such as interpretability, robustness, trustwort
Externí odkaz:
http://arxiv.org/abs/2109.13895
Publikováno v:
In: Moreno-D\'iaz R. et al (eds) Computer Aided Systems Theory, Eurocast 2019. Lecture Notes in Computer Science, Vol. 12013. Springer (2020)
The growing volume of data makes the use of computationally intense machine learning techniques such as symbolic regression with genetic programming more and more impractical. This work discusses methods to reduce the training data and thereby also t
Externí odkaz:
http://arxiv.org/abs/2108.10660
Publikováno v:
In: Moreno-D\'iaz R. et al. Computer Aided Systems Theory. Lecture Notes in Computer Science, Vol. 12013. Springer, 2020, pp 361-369
We introduce in this paper a runtime-efficient tree hashing algorithm for the identification of isomorphic subtrees, with two important applications in genetic programming for symbolic regression: fast, online calculation of population diversity and
Externí odkaz:
http://arxiv.org/abs/2107.10640
Publikováno v:
In Computer Aided Systems Theory - EUROCAST 2019, Series Volume 12013, pp. 370-377. Springer. (2020)
We describe a method for the identification of models for dynamical systems from observational data. The method is based on the concept of symbolic regression and uses genetic programming to evolve a system of ordinary differential equations (ODE). T
Externí odkaz:
http://arxiv.org/abs/2107.06131
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