A parallel evolving algorithm for flexible neural tree
In the past few decades, much success has been achieved in the use of artificial neural networks for classification, recognition, approximation and control. Flexible neural tree (FNT) is a special kind of artificial neural network with flexible tree structures. The most distinctive feature of FNT is its flexible tree structures. This makes it possible for FNT to obtain near-optimal network structures using tree structure optimization algorithms. But the modeling efficiency of FNT is always a problem due to its two-stage optimization. This paper designed a parallel evolving algorithm for FNT (PE-FNT). This algorithm uses PIPE algorithm to optimize tree structures and PSO algorithm to optimize parameters. The evaluation processes of tree structure populations and parameter populations were both parallelized. As an implementation of PE-FNT algorithm, two parallel programs were developed using MPI. A small data set, two medium data sets and three large data sets were applied for the performance evaluations of these programs. Experimental results show that PE-FNT algorithm is an effective parallel FNT algorithm especially for large data sets.
flexible neural tree artificial neural network performance evaluation parallel algorithm