Autori: Elena Bautu; Andrei Bautu; Henri Luchian

Editorial: IEEE Computer Society Press, Ninth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC 2007), 2008.

Rezumat:

Many papers focused on fine-tunning the Gene Expression Programming (GEP) operators or their application rates in order to improve the performances of the algorithm. Much less work was done on optimizing the structural parameters of the chromosomes (i.e. number of genes and gene size). This is probably due to the fact that the No Free Lunch theorem states that no fixed values for these parameters will ever suit all problems. To counteract this fact, this paper presents a modified GEP algorithm, called AdaGEP, which automatically adapts the number of genes used by the chromosome. The adaptation process takes place at chromosome level, allowing chromosomes in the population to evolve with different number of genes.

Cuvinte cheie: programare cu expresii genetica adaptiva, regresie simbolica // adaptive gene expression programming, symbolic regression

URL: http://www.computer.org/portal/web/csdl/doi/10.1109/SYNASC.2007.51