Evolving structure - optimising content

The paper describes the initial results of a new form of evolutionary system specifically designed for time series modeling. The system combines a grammatically-based genetic programming system with various optimisation techniques. The system uses the evolutionary system to construct the structure of equations and optimisation techniques to essentially fill in the details. Three forms of optimisation are described: optimisation of constants in an equation; the optimisation of both the constants and variables in an equation; and the use of a hill-climbing mutation to further tune the evolved and optimised equations. Preliminary results indicate that this combination of techniques produces significant improvements in convergence based on the training data, and produces equivalent generalisation on unseen data, for a given number of population member evaluations.