Zobrazeno 1 - 10
of 42
pro vyhledávání: '"Burlacu, Bogdan"'
Autor:
Russeil, Etienne, de França, Fabrício Olivetti, Malanchev, Konstantin, Burlacu, Bogdan, Ishida, Emille E. O., Leroux, Marion, Michelin, Clément, Moinard, Guillaume, Gangler, Emmanuel
Symbolic regression (SR) searches for analytical expressions representing the relationship between a set of explanatory and response variables. Current SR methods assume a single dataset extracted from a single experiment. Nevertheless, frequently, t
Externí odkaz:
http://arxiv.org/abs/2402.04298
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
Autor:
Burlacu, Bogdan, Kommenda, Michael, Kronberger, Gabriel, Winkler, Stephan, Affenzeller, Michael
Particle-based modeling of materials at atomic scale plays an important role in the development of new materials and understanding of their properties. The accuracy of particle simulations is determined by interatomic potentials, which allow to calcu
Externí odkaz:
http://arxiv.org/abs/2206.06422
Autor:
Burlacu, Bogdan
Non-dominated sorting is a computational bottleneck in Pareto-based multi-objective evolutionary algorithms (MOEAs) due to the runtime-intensive comparison operations involved in establishing dominance relationships between solution candidates. In th
Externí odkaz:
http://arxiv.org/abs/2203.13654
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
Autor:
Kommenda, Michael, Karder, Johannes, Beham, Andreas, Burlacu, Bogdan, Kronberger, Gabriel, Wagner, Stefan, Affenzeller, Michael
Publikováno v:
In: Moreno-D\'iaz R. et al (eds) Computer Aided Systems Theory, Eurocast 2017. Lecture Notes in Computer Science, Vol. 10671. Springer (2018)
Optimization networks are a new methodology for holistically solving interrelated problems that have been developed with combinatorial optimization problems in mind. In this contribution we revisit the core principles of optimization networks and dem
Externí odkaz:
http://arxiv.org/abs/2110.00415
Publikováno v:
In: Moreno-D\'iaz R. et al (eds) Computer Aided Systems Theory, EUROCAST 2015. Lecture Notes in Computer Science, Vol. 9520. Springer (2015)
This paper describes a methodology for analyzing the evolutionary dynamics of genetic programming (GP) using genealogical information, diversity measures and information about the fitness variation from parent to offspring. We introduce a new subtree
Externí odkaz:
http://arxiv.org/abs/2108.10661
Autor:
La Cava, William, Orzechowski, Patryk, Burlacu, Bogdan, de França, Fabrício Olivetti, Virgolin, Marco, Jin, Ying, Kommenda, Michael, Moore, Jason H.
Many promising approaches to symbolic regression have been presented in recent years, yet progress in the field continues to suffer from a lack of uniform, robust, and transparent benchmarking standards. In this paper, we address this shortcoming by
Externí odkaz:
http://arxiv.org/abs/2107.14351
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