abstract = "Genetic programming has been considered a promising
approach for function approximation since it is
possible to optimize both the functional form and the
coefficients. However, it is not easy to find an
optimal set of coefficients by using only
non-adjustable constant nodes in genetic programming.
To overcome the problem, there have been some studies
on genetic programming using adjustable parameters in
linear or non-linear models. Although the nonlinear
parametric model has a merit over the linear parametric
model, there have been few studies on it. In this
paper, we propose a nonlinear parametric genetic
programming which uses a nonlinear gradient method to
estimate parameters. The most notable feature in the
proposed genetic programming is that we design a
parameter attachment algorithm using as few redundant
parameters as possible.",
notes = "GECCO-2006 A joint meeting of the fifteenth
international conference on genetic algorithms
(ICGA-2006) and the eleventh annual genetic programming
conference (GP-2006).