E-LIB Suche - Ergebnisse für: Kommenda, D

1

Evolutionary Algorithms for Segment Optimization in Vectori..:

, In: Proceedings of the Companion Conference on Genetic and Evolutionary Computation,

Fleck, Philipp ; Winkler, Stephan ; Kommenda, Michael... - p. 439-442 , 2023

2

Extended melt‐conveying models for single‐screw extruders: ..:

Marschik, Christian ; Roland, Wolfgang ; Kommenda, Michael
Polymer Engineering & Science. 63 (2023) 11 - p. 3639-3656 , 2023

Link: https://doi.org/10.1002/..

3

Extending a physics-based constitutive model using genetic ..:

Kronberger, Gabriel ; Kabliman, Evgeniya ; Kronsteiner, Johannes.
Applications in Engineering Science. 9 (2022) - p. 100080 , 2022

Link: https://doi.org/10.1016/..

4

Differences in aphasia syndromes between progressive supran..:

Ransmayr, Lucia ; Fuchs, Alexandra ; Ransmayr-Tepser, Sibylle...
Journal of Neural Transmission. 129 (2022) 8 - p. 1039-1048 , 2022

Link: https://doi.org/10.1007/..

5

Shape-Constrained Symbolic Regression—Improving Extrapolati..:

Kronberger, G. ; de Franca, F. O. ; Burlacu, B...
Evolutionary Computation. 30 (2022) 1 - p. 75-98 , 2022

Link: https://doi.org/10.1162/..

6

Application of Symbolic Regression in Polymer Processing:

, In: 2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC),

Roland, Wolfgang ; Kommenda, Michael ; Berger-Weber, Gerald R. - p. 311-318 , 2022

Link: https://doi.org/10.1109/..

7

Application of symbolic regression for constitutive modelin..:

Kabliman, Evgeniya ; Kolody, Ana Helena ; Kronsteiner, Johannes..
Applications in Engineering Science. 6 (2021) - p. 100052 , 2021

Link: https://doi.org/10.1016/..

8

Operon C++ : an efficient genetic programming framework ..:

, In: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion,

Burlacu, Bogdan ; Kronberger, Gabriel ; Kommenda, Michael - p. 1562-1570 , 2020

Link: https://dl.acm.org/doi/1..

9

Parameter identification for symbolic regression using nonl..:

Kommenda, Michael ; Burlacu, Bogdan ; Kronberger, Gabriel.
Genetic Programming and Evolvable Machines. 21 (2019) 3 - p. 471-501 , 2019

Link: https://doi.org/10.1007/..

10

Parsimony measures in multi-objective genetic programming f..:

, In: Proceedings of the Genetic and Evolutionary Computation Conference Companion,

Burlacu, Bogdan ; Kronberger, Gabriel ; Kommenda, Michael. - p. 338-339 , 2019

Link: https://dl.acm.org/doi/1..

11

Extended Regression Models for Predicting the Pumping Capab..:

Roland, Wolfgang ; Kommenda, Michael ; Marschik, Christian.
Polymers. 11 (2019) 2 - p. 334 , 2019

Link: https://doi.org/10.3390/..

12

Using robust generalized fuzzy modeling and enhanced symbol..:

Kronberger, Gabriel ; Kommenda, Michael ; Lughofer, Edwin...
Applied Soft Computing. 69 (2018) - p. 610-624 , 2018

Link: https://doi.org/10.1016/..

13

Predicting friction system performance with symbolic regres..:

, In: Proceedings of the Genetic and Evolutionary Computation Conference,

Kronberger, Gabriel ; Kommenda, Michael ; Promberger, Andreas. - p. 1278-1285 , 2018

Link: https://dl.acm.org/doi/1..

14

Dynamic observation of genotypic and phenotypic diversity f..:

, In: Proceedings of the Genetic and Evolutionary Computation Conference Companion,

Affenzeller, Michael ; Winkler, Stephan M. ; Burlacu, Bogdan... - p. 1553-1558 , 2017

Link: https://dl.acm.org/doi/1..

15

Towards the design and implementation of optimization netwo..:

, In: Proceedings of the Genetic and Evolutionary Computation Conference Companion,

Karder, Johannes ; Wagner, Stefan ; Beham, Andreas.. - p. 1209-1214 , 2017

Link: https://dl.acm.org/doi/1..