Resolution of the inverse problem for iterated function systems using Evolutionary Algorithms

This source preferred by Ari Sarafopoulos

Authors: Sarafopoulos, A. and Buxton, B.

Editors: Yen, G.G., Lucas, S.M., Fogel, G., Kendall, G., Salomon, R., Zhang, B.-T., Coello, C.A. and Runarsson, T.P.

http://ieeexplore.ieee.org/xpl/RecentCon.jsp?punumber=11108

Pages: 1071-1078

Publisher: IEEE Press

Place of Publication: New York

The resolution of the inverse problem for iterated function systems (IFS) is a problem that has remained open, currently there is no general solution that requires no human interaction and provides optimal results. Here we present a novel approach to the resolution of the general inverse problem for IFS using segmentation of target images in conjuction with an Evolutionary Algorithm that is a Genetic Programming-Evolutionary Strategies hybrid

This data was imported from Scopus:

Authors: Sarafopoulos, A. and Buxton, B.

Pages: 1071-1078

ISBN: 9780780394872

The resolution of the inverse problem for iterated function systems (IFS) is a problem that has remained open, currently there is no general solution that requires no human interaction and provides optimal results. Here we present a novel approach to the resolution of the general inverse problem for IFS using segmentation of target images in conjuction with an Evolutionary Algorithm that is a Genetic Programming-Evolutionary Strategies hybrid. © 2006 IEEE.

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

Authors: Sarafopoulos, A., Buxton, B. and IEEE

Pages: 1056-+

ISBN: 978-0-7803-9487-2

The data on this page was last updated at 05:09 on February 27, 2020.