abstract = "In this paper, the problem of genetic regulatory
network inference from time series microarray
experiment data is considered. A noisy sigmoidal model
is proposed to include both system noise and
measurement noise. In order to solve this nonlinear
identification problem (with noise), a joint genetic
algorithm and Kalman filtering approach is proposed.
Genetic algorithm is applied to minimise the fitness
function and Kalman filter is employed to estimate the
weight parameters in each iteration. The effectiveness
of the proposed method is demonstrated by using both
synthetic data and microarray measurements.",