GP-based modeling method for time series prediction with parameter optimization and node alternation

Abstract

A fast method of GP based model building for time series prediction is proposed. The method involves two newly-devised techniques. One is regarding determination of model parameters: only functional forms are inherited from their parents with genetic programming, but model parameters are not inherited. They are optimized by a backpropagation-like algorithm when a child (model) is newborn. The other is regarding mutation: nodes which require a different number of edges, can be transformed into different types of nodes through mutation. This operation is effective at accelerating complicated functions e.g. seismic ground motion. The method has been applied to a typical benchmark of time series and many real world problems.