Sammelbandbeitrag - Wissenschaftlicher Artikel | 2019 | Peer reviewed

Improving NeuroEvolution Efficiency by Surrogate Model-Based Optimization with Phenotypic Distance Kernels

Autor*innen

Stork, Jörg ; Zaefferer, Martin ; Bartz-Beielstein, Thomas

Sammel-/ Konferenzband

Applications of Evolutionary Computation

DOI

10.1007/978-3-030-16692-2

ISBN-13

9783030166922

URL

https://arxiv.org/abs/1902.03419

Weitere Details

Sprache

englisch

Darstellungsform

Text

Seitenbereich

504 - 519

Zitierstile

Stork, J., Zaefferer, M., & Bartz-Beielstein, T. (2019). Improving NeuroEvolution Efficiency by Surrogate Model-Based Optimization with Phenotypic Distance Kernels. In P. Kaufmann & P. A. Castillo, Applications of Evolutionary Computation (pp. 504–519). Cham: Springer. doi:10.1007/978-3-030-16692-2

In die Zwischenablage kopieren

Stork, J., Zaefferer, M. and Bartz-Beielstein, T., 2019. Improving NeuroEvolution Efficiency by Surrogate Model-Based Optimization with Phenotypic Distance Kernels. In: Applications of Evolutionary Computation. [online] Cham: Springer, pp.504–519. Available at: <https://arxiv.org/abs/1902.03419>.

In die Zwischenablage kopieren

Stork, Jörg, Zaefferer, Martin & Bartz-Beielstein, Thomas 2019. Improving NeuroEvolution Efficiency by Surrogate Model-Based Optimization with Phenotypic Distance Kernels. In Applications of Evolutionary Computation. Cham: Springer, 504–519. https://arxiv.org/abs/1902.03419.

American Psychological Association 6th edition (DOI)

Anglia Ruskin University - Harvard

Gesellschaft für Bildung und Forschung in Europa - Harvard