Empirical comparison of evolutionary representations of the inverse problem for iterated function systems

This source preferred by Ari Sarafopoulos

Authors: Sarafopoulos, A. and Buxton, B.

Editors: Ebner, M., O'Neill, M., Vanneschi, L., Ekart, A. and Esparcia Alcázar, I.

Pages: 68-77

Publisher: Springer-Verlag

Place of Publication: Berlin, Heidelberg

ISBN: 978-3-540-71602-0

DOI: 10.1007/978-3-540-71605-1_7

In this paper we present an empirical comparison between evolutionary representations for the resolution of the inverse problem for iterated function systems (IFS). We introduce a class of problem instances that can be used for the comparison of the inverse IFS problem as well as a novel technique that aids exploratory analysis of experiment data. Our comparison suggests that representations that exploit problem specific information, apart from quality/fitness feedback, perform better for the resolution of the inverse problem for IFS.

This data was imported from DBLP:

Authors: Sarafopoulos, A. and Buxton, B.

Editors: Ebner, M., O'Neill, M., Ekárt, A., Vanneschi, L. and Esparcia-Alcázar, A.

https://doi.org/10.1007/978-3-540-71605-1

Volume: 4445

Pages: 68-77

Publisher: Springer

ISBN: 978-3-540-71602-0

This data was imported from Scopus:

Authors: Sarafopoulos, A. and Buxton, B.

Volume: 4445 LNCS

Pages: 68-77

ISBN: 9783540716020

In this paper we present an empirical comparison between evolutionary representations for the resolution of the inverse problem for iterated function systems (IFS). We introduce a class of problem instances that can be used for the comparison of the inverse IFS problem as well as a novel technique that aids exploratory analysis of experiment data. Our comparison suggests that representations that exploit problem specific information, apart from quality/fitness feedback, perform better for the resolution of the inverse problem for IFS. © Springer-Verlag Berlin Heidelberg 2007.

This data was imported from Web of Science (Lite):

Authors: Sarafopoulos, A., Buxton, B. and Ebner, M.

Volume: 4445

Pages: 68-+

ISBN: 978-3-540-71602-0

The data on this page was last updated at 05:13 on February 22, 2020.