Title :
Symbolic regression via genetic programming
Author :
Augusto, Douglas A. ; Barbosa, Helio J C
Author_Institution :
Lab. Nacional de Comput. Cientifica, Rio de Janeiro, Brazil
Abstract :
Presents an implementation of symbolic regression which is based on genetic programming (GP). Unfortunately, standard implementations of GP in compiled languages are not usually the most efficient ones. The present approach employs a simple representation for tree-like structures by making use of Read´s linear code, leading to more simplicity and better performance when compared with traditional GP implementations. Creation, crossover and mutation of individuals are formalized. An extension allowing for the creation of random coefficients is presented. The efficiency of the proposed implementation was confirmed in computational experiments which are summarized in the paper
Keywords :
genetic algorithms; linear codes; probability; tree searching; Read´s linear code; computational experiments; creation; crossover; genetic programming; mutation; random coefficients; symbolic regression; tree-like structures; Biological information theory; Genetic algorithms; Genetic mutations; Genetic programming; Linear code; Predictive models; Vectors;
Conference_Titel :
Neural Networks, 2000. Proceedings. Sixth Brazilian Symposium on
Conference_Location :
Rio de Janeiro, RJ
Print_ISBN :
0-7695-0856-1
DOI :
10.1109/SBRN.2000.889734