GENETIC PROGRAMMING WITH EITHER STOCHASTIC OR DETERMINISTIC CONSTANT EVALUATION

V. Hlavac

《Neural Network World》 28卷2期 (2018/04) Pp. 119-131

https://doi.org/10.14311/NNW.2018.28.007

摘要

Constant evaluation is a key problem for symbolic regression, one solved by means of genetic programming. For constant evaluation, other evolutionary methods are often used. Typical examples are some variants of genetic programming or evolutionary systems, all of which are stochastic. The article compares these methods with a deterministic approach using exponentiated gradient descent. All the methods were tested on single sample function to maintain the same conditions and results are presented in graphs. Finally, three different tasks (ten times each) are compared to check the reliability of the methods tested in the article.

關鍵字

genetic programming ； constant evaluation ； gradient descent ； symbolic regression