Genetic programming with dynamic fitness for a remote sensing application

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1917, Issue , 2000, Pages 191-200

  Fonlupt, Cyril ^a   Robilliard, Denis ^a  

^a UNIVERSITÉ DU LITTORAL CÔTE D OPALE   (France)

Author keywords

[No Author keywords available]

Indexed keywords

GENETIC ALGORITHMS; GREENHOUSE EFFECT; PHYTOPLANKTON; PROBLEM SOLVING; REMOTE SENSING;

ARTIFICIAL NEURAL NET; COMPUTING TIME; ESTIMATION ERRORS; LUMINANCE VALUE; NEW SATELLITES; PROGRESSIVE LEARNING; REGRESSION PROBLEM; REMOTE SENSING APPLICATIONS;

GENETIC PROGRAMMING;

EID: 35048850607     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45356-3_19     Document Type: Conference Paper

References (18)

1. Manu Ahluwalia and Larry Bull. Coevolving functions in genetic programming: classification using k-nearest-neighbour. In [2], pages 947-952, 1999.
2. Wolfgang Banzhaf, Jason Daida, Agoston Eibenand Max Garzón, Vasant Honavar, Mark Jakiela, and Robert Smith, editors. Proceedings of the Genetic and Evolutionary Computation Conference, Orlando, Florida, USA, july 1999. MorganKaufmann.
3. Wolfgang Banzhaf, Peter Nordin, Robert Keller, and Frank Francone. Genetic Programming An Introduction. Morgan Kaufmann, 1999.
4. Malik Chami. Développement d'un code de transfert radiatif pour le système océan-atmosphère. Application au détroit du Pas de Calais. PhD thesis, Université du Littoral - Côte d'Opale, 1997. in french.
5. Malik Chami, Eric Dilligeard, and Richard Santer. A radiative transfer code for the sea-atmosphere system. In Remote Sensing of Ocean and Sea Ice III, volume 3222 of Earth Surface Remote Sensing, Aerospace Remote Sensing, SPIE EUROPTO, 1997.
6. J Daida, T.F. Bersano-Begey, S.J. Ross, and J.F Vesecky. Computer-assisted design of image classification algorithms: dynamic and static fitness evaluations in a scaffolded genetic programming environment. In [13], pages 279-284, 1996.
7. J. Daida, J.D. Hommes, T.F. Bersano-Begey, S.J. Ross, and J.F. Vesecky. Advances in Genetic Programming 2, chapter Algorithm discovery using the genetic programming paradigm: extracting low-contrast curvilinear features from the SAR images of the artic ice, pages 417-442. MIT Press, 1996.
8. Stephanie Forrest, editor. Proceeding of the 5th International Conference on Genetic Algorithms, University of Illinois, Urbana - Champaign, USA, July 1993. Morgan Kaufmann.
9. Chris GatherCole and Peter Ross. Tackling the boolean even N parity problem with genetic programming and limited-error fitness. In Proceedings of the Second Annual Conference on Genetic Programming. Morgan Kauffmann, 1997.
10. Lidwine Gross, Sylvie Thiria, and Robert Frouin. Applying artificial neural network methodology to ocean color remote sensing. Ecological Modelling, 120:237-246, 1999.
11. LW Harding, BW Meeson, and TR Fisher. Phytoplankton production in two east coast estuaries: photosynthesislight functions and patterns of carbon assimilation in Chesapeake and delaware bays. Estuar Coast Shelf Sci, 23:773-806, 1986.
12. Daniel Howard and Simon C. Roberts. Staged genetic programming strategy for image analysis. In [2], pages 1047-1052, 1999.
13. Koza, Goldberg, Fogel and Riolo, editors. Proceedings of the First Annual Conference on Genetic Programming, Stanford University, CA, LISA, 1996. MIT Press.
14. A. Morel. Optical modeling of the upper ocean in relation to its biogenous matter content (case 1 waters). J. Geophys. Res., C9(93):10749-10768, 1988.
15. A. Morel. Light and marine photosynthesis: a spectral model with geochemical and climatological implications. Prog. Oceanogr., 26:263-306, 1991.
16. Riccardo Poli. Genetic programming for image analysis. In [13], pages 363-368, 1996.
17. H. Schiller and R. Doerffer. Neural network for emulation of an inverse model-operational derivation of case II properties from MERIS. International Journal of Remote Sensing, 20(9):1735-1746, 1998.
18. Walter Alden Tackett. genetic programming for feature discovery and image discrimination. In [8], pages 303-309, 1993.