Multi-branches Genetic Programming as a Tool for Function Approximation

Katya Rodríguez-Vázquez and Carlos Oliver-Morales

Abstract. This work presents a performance analysis of a Multi-Branches Genetic Programming (MBGP) approach applied in symbolic regression (e.g. function approximation) problems. Genetic Programming (GP) has been previously applied to this kind of regression. However, one of the main drawbacks of GP is the fact that individuals tend to grow in size through the evolution process without a significant improvement in individual performance. In Multi-Branches Genetic Programming (MBGP), an individual is composed of several branches, each branch can evolve a part of individual solution, and final solution is composed of the integration of these partial solutions.

LNCS 3103, p. 719 ff.

Full article in PDF

lncs@springer.de
© Springer-Verlag Berlin Heidelberg 2004