DGP: How To Improve Genetic Programming with Duals
Created by W.Langdon from
gpbibliography.bib Revision:1.4910
 @InProceedings{Segapeli:1997:DGP,

author = "JL Segapeli and C. Escazut and P. Collard",

title = "DGP: How To Improve Genetic Programming with Duals",

booktitle = "Artificial Neural Nets and Genetic Algorithms:
Proceedings of the International Conference,
ICANNGA97",

year = "1997",

editor = "George D. Smith and Nigel C. Steele and
Rudolf F. Albrecht",

pages = "409413",

address = "University of East Anglia, Norwich, UK",

publisher = "SpringerVerlag",

note = "published in 1998",

keywords = "genetic algorithms, genetic programming",

ISBN = "3211830871",

DOI = "doi:10.1007/9783709164921_90",

abstract = "In this paper, we present a new approach, improving
the performances of a genetic algorithm (GA). Such
algorithms are iterative search procedures based on
natural genetics. We use an original genetic algorithm
that manipulates pairs of twins in its population: DGA,
dualbased genetic algorithm. We show that this
approach is relevant for genetic programming (GP),
which manipulates populations of trees. In particular,
we show that duals can transform a deceptive problem
into a convergent one. We also prove that using pairs
of dual functions in the primitive function set, is
more efficient in the problem of learning boolean
functions. Here, in order to prove the theoretical
interest of our approach (DGP: dualbased genetic
programming), we perform a numerical simulation.",

notes = "http://www.sys.uea.ac.uk/Research/ResGroups/MAG/ICANNGA97/papers_frame.html
http://www.springer.com/sgw/cda/frontpage/0,11855,11022215958760,00.html?changeHeader=true",
 }
Genetic Programming entries for
JL Segapeli
Cathy Escazut
Philippe Collard
Citations