Symbolic regression via genetic programming

In this work we present 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 treelike 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 this paper.