DocumentCode :
2702756
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
fYear :
2000
fDate :
2000
Firstpage :
173
Lastpage :
178
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;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2000. Proceedings. Sixth Brazilian Symposium on
Conference_Location :
Rio de Janeiro, RJ
ISSN :
1522-4899
Print_ISBN :
0-7695-0856-1
Type :
conf
DOI :
10.1109/SBRN.2000.889734
Filename :
889734
Link To Document :
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