A~metaheuristic GAs method as a~decision support for the choice of cancer treatment
Sibirskij žurnal vyčislitelʹnoj matematiki, Tome 7 (2004) no. 4, pp. 301-307.

Voir la notice de l'article provenant de la source Math-Net.Ru

This paper focuses on a metaheuristic method that helps in evaluating the cancer treatment complexity. We show how to help find a (near) optimal treatment formula by using a genetic algorithms japproach. When the diagnosis problem has been solved, attention is given to designing the treatment procedure. The goal of this paper is to explore a GA-based approach to determine the (near) optimum treatment formula depending on some features of the patient. An application to breast and uterus cancers is presented as well.
@article{SJVM_2004_7_4_a2,
     author = {F. Gorunescu and M. Gorunescu and R. Gorunescu},
     title = {A~metaheuristic {GAs} method as a~decision support for the choice of cancer treatment},
     journal = {Sibirskij \v{z}urnal vy\v{c}islitelʹnoj matematiki},
     pages = {301--307},
     publisher = {mathdoc},
     volume = {7},
     number = {4},
     year = {2004},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/SJVM_2004_7_4_a2/}
}
TY  - JOUR
AU  - F. Gorunescu
AU  - M. Gorunescu
AU  - R. Gorunescu
TI  - A~metaheuristic GAs method as a~decision support for the choice of cancer treatment
JO  - Sibirskij žurnal vyčislitelʹnoj matematiki
PY  - 2004
SP  - 301
EP  - 307
VL  - 7
IS  - 4
PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/item/SJVM_2004_7_4_a2/
LA  - en
ID  - SJVM_2004_7_4_a2
ER  - 
%0 Journal Article
%A F. Gorunescu
%A M. Gorunescu
%A R. Gorunescu
%T A~metaheuristic GAs method as a~decision support for the choice of cancer treatment
%J Sibirskij žurnal vyčislitelʹnoj matematiki
%D 2004
%P 301-307
%V 7
%N 4
%I mathdoc
%U http://geodesic.mathdoc.fr/item/SJVM_2004_7_4_a2/
%G en
%F SJVM_2004_7_4_a2
F. Gorunescu; M. Gorunescu; R. Gorunescu. A~metaheuristic GAs method as a~decision support for the choice of cancer treatment. Sibirskij žurnal vyčislitelʹnoj matematiki, Tome 7 (2004) no. 4, pp. 301-307. http://geodesic.mathdoc.fr/item/SJVM_2004_7_4_a2/

[1] M. Abellof, J. Armitage, A. Lichter, J. Niederhuber (eds.), Clinical Oncology, 2nd ed., Churchill, Livingstone, 2000

[2] Hess D. J., Evaluating Alternative Cancer Therapies: A Guide to the Science and Politics of an Emerging Medical Field, Rutgers University Press, 1999

[3] Pech-Gourg N., Hao Jin-Kao, “A genetic algorithm for the classification of natural corks”, Proc. GECCO2001, San Francisco, 2001, 1382–1388

[4] HollandJ. H., Adaptation in natural and artificial systems, The University of Michigan Press, Ann Arbor, 1975 | MR

[5] Goldberg D. E., Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Reading, MA, 1989

[6] Michalewicz Z., Genetic Algorithms + Data Structures = Evolution Programs, 2nd ed., extended, Springer-Verlag, 1994 | MR | Zbl

[7] FogelD. B., Evolutionary Computation: Toward a New Philosophy of Machine, IEEE Press Series on Computational Intelligence, IEEE Press, Piscataway, NJ, 1995 | MR

[8] Dumitrescu D., Genetic algorithms and evolution strategies – Applications in Artificial Intelligence and connex domains / Microinformatica, Blue Publishing House, 2000 (in Romanian)

[9] Gorunescu F., Gaman G., Gorunescu M., Gaman A., Ciurea T., Ciurea P., “A classification and regression tree technique applied in acute leukemia”, Leukemia and Limphoma. Proc. International Conference “Leukemia towards the cure 2002” (Miami, USA, September 19–22, 2002), 2002, 77